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    <title>Pinboard (Vaguery)</title>
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    <description>recent bookmarks from Vaguery</description>
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	<rdf:li rdf:resource="https://arxiv.org/abs/2110.14237"/>
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	<rdf:li rdf:resource="https://arxiv.org/abs/2211.12589"/>
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	<rdf:li rdf:resource="https://arxiv.org/abs/1810.04735"/>
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	<rdf:li rdf:resource="https://arxiv.org/abs/1705.08756"/>
	<rdf:li rdf:resource="http://www.openhumanitiespress.org/books/titles/machine-sensation/"/>
	<rdf:li rdf:resource="https://www.nature.com/articles/s41598-020-73380-x"/>
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	<rdf:li rdf:resource="https://openai.com/blog/learning-dexterity/"/>
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	<rdf:li rdf:resource="https://openai.com/blog/emergent-tool-use/"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1809.02836"/>
	<rdf:li rdf:resource="https://muircheartblog.wordpress.com/2019/06/07/does-art-compute/"/>
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	<rdf:li rdf:resource="https://arxiv.org/abs/1707.06631"/>
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	<rdf:li rdf:resource="https://arxiv.org/abs/1603.08269"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1012.1332"/>
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  </channel><item rdf:about="https://arxiv.org/abs/2509.18522">
    <title>[2509.18522] Functional Information Decomposition: A First-Principles Approach to Analyzing Functional Relationships</title>
    <dc:date>2026-05-24T17:24:25+00:00</dc:date>
    <link>https://arxiv.org/abs/2509.18522</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[A central challenge in analyzing multivariate interactions within complex systems is to decompose how multiple inputs jointly determine an output. Existing approaches generally operate on observed probability distributions and can conflate a system's intrinsic functional logic with statistical artifacts of limited data. As a result, distinct systems can yield identical observations, rendering information decomposition fundamentally underdetermined and obscuring true higher-order interactions.
We introduce Functional Information Decomposition (FID), both a computational and theoretical framework, which defines informational components with respect to a system's complete input-output mapping, thereby addressing a core cross-scale inference problem: determining how information carried by individual components combines to shape system-level behavior. When the mapping is fully specified, FID provides a unique decomposition into independent and synergistic contributions. Crucially, given only partial observations, FID characterizes the entire space of consistent decompositions by sampling compatible functions, making inferential limits explicit. A complementary geometric perspective clarifies the structural origin of informational components.
We demonstrate FID's interdisciplinary utility on canonical logical functions, Conway's Game of Life, and gene-expression-based prediction of cancer drug response, and provide an open-source implementation. By separating functional architecture from observational distribution, FID offers a principled foundation for analyzing multivariate dependence in both fully and partially observed complex systems.
]]></description>
<dc:subject>information-theory artificial-life physics randomness hey-I-know-this-guy function philosophy-of-science</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:7b913a762d18/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:physics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:randomness"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hey-I-know-this-guy"/>
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<item rdf:about="https://arxiv.org/abs/1208.0482">
    <title>[1208.0482] The concurrent evolution of cooperation and the population structures that support it</title>
    <dc:date>2026-05-24T12:27:54+00:00</dc:date>
    <link>https://arxiv.org/abs/1208.0482</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The evolution of cooperation often depends upon population structure, yet nearly all models of cooperation implicitly assume that this structure remains static. This is a simplifying assumption, because most organisms possess genetic traits that affect their population structure to some degree. These traits, such as a group size preference, affect the relatedness of interacting individuals and hence the opportunity for kin or group selection. We argue that models that do not explicitly consider their evolution cannot provide a satisfactory account of the origin of cooperation, because they cannot explain how the prerequisite population structures arise. Here, we consider the concurrent evolution of genetic traits that affect population structure, with those that affect social behavior. We show that not only does population structure drive social evolution, as in previous models, but that the opportunity for cooperation can in turn drive the creation of population structures that support it. This occurs through the generation of linkage disequilibrium between socio-behavioral and population-structuring traits, such that direct kin selection on social behavior creates indirect selection pressure on population structure. We illustrate our argument with a model of the concurrent evolution of group size preference and social behavior.
]]></description>
<dc:subject>artificial-life machine-learning complexology rather-interesting hey-I-know-this-guy theoretical-biology to-simulate consider:performance-measures coevolution</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:b7f06f7b43d1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:complexology"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hey-I-know-this-guy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:theoretical-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
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<item rdf:about="https://www.nature.com/articles/s41557-025-01981-y">
    <title>A recursive enzymatic competition network capable of multitask molecular information processing | Nature Chemistry</title>
    <dc:date>2025-12-10T14:31:54+00:00</dc:date>
    <link>https://www.nature.com/articles/s41557-025-01981-y</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Living cells understand their environment by combining, integrating and interpreting chemical and physical stimuli. Despite considerable advances in the design of enzymatic reaction networks that mimic hallmarks of living systems, these approaches lack the complexity to fully capture biological information processing. Here we introduce a scalable approach to design complex enzymatic reaction networks capable of reservoir computation based on recursive competition of substrates. This protease-based network can perform a broad range of classification tasks based on peptide and physicochemical inputs and can simultaneously perform an extensive set of discrete and continuous information processing tasks. The enzymatic reservoir can act as a temperature sensor from 25 °C to 55 °C with 1.3 °C accuracy, and performs decision-making, activation and tuning tasks common to neurological systems. We show a possible route to temporal information processing and a direct interface with optical systems by demonstrating the extension of the network to incorporate sensitivity to light pulses. Our results show a class of competition-based molecular systems capable of increasingly powerful information-processing tasks.

]]></description>
<dc:subject>reaction-networks artificial-life reservoir-computing biochemistry nonlinear-dynamics indistinguishable-from-magic to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:ed6e937bb4ae/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:reservoir-computing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nonlinear-dynamics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:indistinguishable-from-magic"/>
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<item rdf:about="https://arxiv.org/abs/2401.05375">
    <title>[2401.05375] Classical Sorting Algorithms as a Model of Morphogenesis: self-sorting arrays reveal unexpected competencies in a minimal model of basal intelligence</title>
    <dc:date>2025-04-05T21:13:42+00:00</dc:date>
    <link>https://arxiv.org/abs/2401.05375</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The emerging field of Diverse Intelligence seeks to identify, formalize, and understand commonalities in behavioral competencies across a wide range of implementations. Especially interesting are simple systems that provide unexpected examples of memory, decision-making, or problem-solving in substrates that at first glance do not appear to be complex enough to implement such capabilities. We seek to develop tools to help understand the minimal requirements for such capabilities, and to learn to recognize and predict basal forms of intelligence in unconventional substrates. Here, we apply novel analyses to the behavior of classical sorting algorithms, short pieces of code which have been studied for many decades. To study these sorting algorithms as a model of biological morphogenesis and its competencies, we break two formerly-ubiquitous assumptions: top-down control (instead, showing how each element within a array of numbers can exert minimal agency and implement sorting policies from the bottom up), and fully reliable hardware (instead, allowing some of the elements to be "damaged" and fail to execute the algorithm). We quantitatively characterize sorting activity as the traversal of a problem space, showing that arrays of autonomous elements sort themselves more reliably and robustly than traditional implementations in the presence of errors. Moreover, we find the ability to temporarily reduce progress in order to navigate around a defect, and unexpected clustering behavior among the elements in chimeric arrays whose elements follow one of two different algorithms. The discovery of emergent problem-solving capacities in simple, familiar algorithms contributes a new perspective to the field of Diverse Intelligence, showing how basal forms of intelligence can emerge in simple systems without being explicitly encoded in their underlying mechanics.
]]></description>
<dc:subject>artificial-life complexology rather-interesting problem-solving robustness to-write-about consider:evolved-solutions consider:local-and-global consider:performance-measures</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:181ddb4e6470/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:problem-solving"/>
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<item rdf:about="https://arxiv.org/abs/2407.03345">
    <title>[2407.03345] An Open-Ended Approach to Understanding Local, Emergent Conservation Laws in Biological Evolution</title>
    <dc:date>2024-12-21T16:33:13+00:00</dc:date>
    <link>https://arxiv.org/abs/2407.03345</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[While fields like Artificial Life have made huge strides in quantifying the mechanisms that distinguish living systems from non-living ones, particular mechanisms remain difficult to reproduce in silico. Known as open-endedness, we've been successful in finding mechanisms that generate new states, but have been less successful in finding mechanisms that generate new rules. Here, we weigh whether or not analyzing the effects of internal and external system constraints on a system's dynamics would be a fruitful avenue to understanding open-endedness. We discuss the connection between physical constraints and the ways that the system can physically reach possible states while those constraints are present. It seems that the physical constraints that define biological objects (and dynamics) are maintained by dynamics that occur from within the system. This is in opposition to current modeling approaches where system constraints are maintained externally. We suggest that constraints can be characterized as variables whose values are either completely conserved, quasi-conserved, or conditionally conserved. Regardless of whether or not a constrained variable is a part of the biological object or present in the object's environment, we discuss how the accessible system states under that constraint can lead to local, emergent conservation laws (rules), with examples. Finally, we discuss the possible benefits of formally understanding how system constraints that emerge from within a system lead to system dynamics that can be characterized as new, emergent rules -- particularly for artificial intelligence, hybrid life, embodiment, astrobiology, and more. Understanding how new, local rules might emerge from within the system is crucial for understanding how open-ended systems continually discover new update rules, in addition to continually discovering new states.
]]></description>
<dc:subject>artificial-life systems-biology origin-of-life theoretical-biology space-still-matters to-write-about to-simulate consider:genetic-programming</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:ca76765b60df/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:systems-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:origin-of-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:theoretical-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:space-still-matters"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:genetic-programming"/>
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<item rdf:about="https://arxiv.org/abs/2411.12304">
    <title>[2411.12304] Emergence of Implicit World Models from Mortal Agents</title>
    <dc:date>2024-12-21T16:31:28+00:00</dc:date>
    <link>https://arxiv.org/abs/2411.12304</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We discuss the possibility of world models and active exploration as emergent properties of open-ended behavior optimization in autonomous agents. In discussing the source of the open-endedness of living things, we start from the perspective of biological systems as understood by the mechanistic approach of theoretical biology and artificial life. From this perspective, we discuss the potential of homeostasis in particular as an open-ended objective for autonomous agents and as a general, integrative extrinsic motivation. We then discuss the possibility of implicitly acquiring a world model and active exploration through the internal dynamics of a network, and a hypothetical architecture for this, by combining meta-reinforcement learning, which assumes domain adaptation as a system that achieves robust homeostasis.
]]></description>
<dc:subject>autopoiesis open-ended-evolution artificial-life machine-learning algorithms to-read to-understand philosophy-of-engineering</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:24353b147828/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:autopoiesis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:open-ended-evolution"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-understand"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy-of-engineering"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/0912.3464">
    <title>[0912.3464] Self-assembly, modularity and physical complexity</title>
    <dc:date>2024-10-08T20:15:47+00:00</dc:date>
    <link>https://arxiv.org/abs/0912.3464</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We present a quantitative measure of physical complexity, based on the amount of information required to build a given physical structure through self-assembly. Our procedure can be adapted to any given geometry, and thus to any given type of physical system. We illustrate our approach using self-assembling polyominoes, and demonstrate the breadth of its potential applications by quantifying the physical complexity of molecules and protein complexes. This measure is particularly well suited for the detection of symmetry and modularity in the underlying structure, and allows for a quantitative definition of structural modularity. Furthermore we use our approach to show that symmetric and modular structures are favoured in biological self-assembly, for example of protein complexes. Lastly, we also introduce the notions of joint, mutual and conditional complexity, which provide a useful distance measure between physical structures.
]]></description>
<dc:subject>self-assembly root-reference artificial-life to-write-about to-simulate consider:enumeration</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:6d9f9fa52eb9/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:enumeration"/>
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</item>
<item rdf:about="https://arxiv.org/abs/2206.14916">
    <title>[2206.14916] The Hiatus Between Organism and Machine Evolution: Contrasting Mixed Microbial Communities with Robots</title>
    <dc:date>2024-10-08T19:45:34+00:00</dc:date>
    <link>https://arxiv.org/abs/2206.14916</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Mixed microbial communities, usually composed of various bacterial and fungal species, are fundamental in a plethora of environments, from soil to human gut and skin. Their evolution is a paradigmatic example of intertwined dynamics, where not just the relations among species plays a role, but also the opportunities -- and possible harms -- that each species presents to the others. These opportunities are in fact \textit{affordances}, which can be seized by heritable variation and selection. In this paper, starting from a systemic viewpoint of mixed microbial communities, we focus on the pivotal role of affordances in evolution and we contrast it to the artificial evolution of programs and robots. We maintain that the two realms are neatly separated, in that natural evolution proceeds by extending the space of its possibilities in a completely open way, while the latter is inherently limited by the algorithmic framework it is defined. This discrepancy characterises also an envisioned setting in which robots evolve in the physical world. We present arguments supporting our claim and we propose an experimental setting for assessing our statements. Rather than just discussing the limitations of the artificial evolution of machines, the aim of this contribution is to emphasize the tremendous potential of the evolution of the biosphere, beautifully represented by the evolution of communities of microbes.
]]></description>
<dc:subject>philosophy-of-science philosophy-of-engineering hey-I-know-this-guy artificial-life open-endedness define-your-terms not-wrong to-write-about to-respond-more-clearly-to</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:17e72b533899/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy-of-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy-of-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hey-I-know-this-guy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:open-endedness"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:define-your-terms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:not-wrong"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-respond-more-clearly-to"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2309.10201">
    <title>[2309.10201] Evolving generalist controllers to handle a wide range of morphological variations</title>
    <dc:date>2024-09-21T14:03:08+00:00</dc:date>
    <link>https://arxiv.org/abs/2309.10201</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Neuro-evolutionary methods have proven effective in addressing a wide range of tasks. However, the study of the robustness and generalizability of evolved artificial neural networks (ANNs) has remained limited. This has immense implications in the fields like robotics where such controllers are used in control tasks. Unexpected morphological or environmental changes during operation can risk failure if the ANN controllers are unable to handle these changes. This paper proposes an algorithm that aims to enhance the robustness and generalizability of the controllers. This is achieved by introducing morphological variations during the evolutionary training process. As a results, it is possible to discover generalist controllers that can handle a wide range of morphological variations sufficiently without the need of the information regarding their morphologies or adaptation of their parameters. We perform an extensive experimental analysis on simulation that demonstrates the trade-off between specialist and generalist controllers. The results show that generalists are able to control a range of morphological variations with a cost of underperforming on a specific morphology relative to a specialist. This research contributes to the field by addressing the limited understanding of robustness and generalizability and proposes a method by which to improve these properties.
]]></description>
<dc:subject>artificial-life robotics generalization overfitting evolutionary-algorithms rather-interesting to-write-about to-simulate</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:99650ce0cd60/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generalization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:overfitting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:evolutionary-algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2110.14237">
    <title>[2110.14237] Learning Graph Cellular Automata</title>
    <dc:date>2024-08-08T13:37:36+00:00</dc:date>
    <link>https://arxiv.org/abs/2110.14237</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Cellular automata (CA) are a class of computational models that exhibit rich dynamics emerging from the local interaction of cells arranged in a regular lattice. In this work we focus on a generalised version of typical CA, called graph cellular automata (GCA), in which the lattice structure is replaced by an arbitrary graph. In particular, we extend previous work that used convolutional neural networks to learn the transition rule of conventional CA and we use graph neural networks to learn a variety of transition rules for GCA. First, we present a general-purpose architecture for learning GCA, and we show that it can represent any arbitrary GCA with finite and discrete state space. Then, we test our approach on three different tasks: 1) learning the transition rule of a GCA on a Voronoi tessellation; 2) imitating the behaviour of a group of flocking agents; 3) learning a rule that converges to a desired target state.
]]></description>
<dc:subject>cellular-automata artificial-life engineering-design rather-interesting graph-theory to-understand to-simulate consider:graph-algorithms consider:distributed-computing consider:ReQ</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:457fe2d84025/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cellular-automata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:engineering-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:graph-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-understand"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:graph-algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:distributed-computing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:ReQ"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2406.19108">
    <title>[2406.19108] Computational Life: How Well-formed, Self-replicating Programs Emerge from Simple Interaction</title>
    <dc:date>2024-07-13T11:45:00+00:00</dc:date>
    <link>https://arxiv.org/abs/2406.19108</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The fields of Origin of Life and Artificial Life both question what life is and how it emerges from a distinct set of "pre-life" dynamics. One common feature of most substrates where life emerges is a marked shift in dynamics when self-replication appears. While there are some hypotheses regarding how self-replicators arose in nature, we know very little about the general dynamics, computational principles, and necessary conditions for self-replicators to emerge. This is especially true on "computational substrates" where interactions involve logical, mathematical, or programming rules. In this paper we take a step towards understanding how self-replicators arise by studying several computational substrates based on various simple programming languages and machine instruction sets. We show that when random, non self-replicating programs are placed in an environment lacking any explicit fitness landscape, self-replicators tend to arise. We demonstrate how this occurs due to random interactions and self-modification, and can happen with and without background random mutations. We also show how increasingly complex dynamics continue to emerge following the rise of self-replicators. Finally, we show a counterexample of a minimalistic programming language where self-replicators are possible, but so far have not been observed to arise.
]]></description>
<dc:subject>artificial-life is-it-soup-yet? artificial-chemistries rather-interesting esoteric-languages looking-to-see origin-of-life self-organization automata to-write-about to-cite via:lana-sinapayan</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:b4c65f1d4001/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:is-it-soup-yet?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-chemistries"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:esoteric-languages"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:looking-to-see"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:origin-of-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:self-organization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:automata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-cite"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:via:lana-sinapayan"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2211.12589">
    <title>[2211.12589] Building Squares with Optimal State Complexity in Restricted Active Self-Assembly</title>
    <dc:date>2024-03-29T14:40:38+00:00</dc:date>
    <link>https://arxiv.org/abs/2211.12589</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Tile Automata is a recently defined model of self-assembly that borrows many concepts from cellular automata to create active self-assembling systems where changes may be occurring within an assembly without requiring attachment. This model has been shown to be powerful, but many fundamental questions have yet to be explored. Here, we study the state complexity of assembling n×n squares in seeded Tile Automata systems where growth starts from a seed and tiles may attach one at a time, similar to the abstract Tile Assembly Model. We provide optimal bounds for three classes of seeded Tile Automata systems (all without detachment), which vary in the amount of complexity allowed in the transition rules. We show that, in general, seeded Tile Automata systems require Θ(log14n) states. For single-transition systems, where only one state may change in a transition rule, we show a bound of Θ(log13n), and for deterministic systems, where each pair of states may only have one associated transition rule, a bound of Θ((lognloglogn)12).
]]></description>
<dc:subject>self-assembly cellular-automata tile-automata artificial-life to-write-about to-simulate to-animate rather-interesting consider:tangrams</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:b83ea9922582/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:self-assembly"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cellular-automata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:tile-automata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-animate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:tangrams"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1804.02508">
    <title>[1804.02508] Evolution leads to a diversity of motion-detection neuronal circuits</title>
    <dc:date>2022-01-12T23:11:25+00:00</dc:date>
    <link>https://arxiv.org/abs/1804.02508</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[A central goal of evolutionary biology is to explain the origins and distribution of diversity across life. Beyond species or genetic diversity, we also observe diversity in the circuits (genetic or otherwise) underlying complex functional traits. However, while the theory behind the origins and maintenance of genetic and species diversity has been studied for decades, theory concerning the origin of diverse functional circuits is still in its infancy. It is not known how many different circuit structures can implement any given function, which evolutionary factors lead to different circuits, and whether the evolution of a particular circuit was due to adaptive or non-adaptive processes. Here, we use digital experimental evolution to study the diversity of neural circuits that encode motion detection in digital (artificial) brains. We find that evolution leads to an enormous diversity of potential neural architectures encoding motion detection circuits, even for circuits encoding the exact same function. Evolved circuits vary in both redundancy and complexity (as previously found in genetic circuits) suggesting that similar evolutionary principles underlie circuit formation using any substrate. We also show that a simple (designed) motion detection circuit that is optimally-adapted gains in complexity when evolved further, and that selection for mutational robustness led this gain in complexity.
]]></description>
<dc:subject>artificial-life markov-brains hey-I-know-this-guy to-write-about to-simulate consider:web-interfaces</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:ae0c74c48806/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:markov-brains"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hey-I-know-this-guy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:web-interfaces"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1810.04735">
    <title>[1810.04735] Towards the Targeted Environment-Specific Evolution of Robot Components</title>
    <dc:date>2021-11-04T10:28:05+00:00</dc:date>
    <link>https://arxiv.org/abs/1810.04735</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[This research considers the task of evolving the physical structure of a robot to enhance its performance in various environments, which is a significant problem in the field of Evolutionary Robotics. Inspired by the fields of evolutionary art and sculpture, we evolve only targeted parts of a robot, which simplifies the optimisation problem compared to traditional approaches that must simultaneously evolve both (actuated) body and brain. Exploration fidelity is emphasised in areas of the robot most likely to benefit from shape optimisation, whilst exploiting existing robot structure and control. Our approach uses a Genetic Algorithm to optimise collections of Bezier splines that together define the shape of a legged robot's tibia, and leg performance is evaluated in parallel in a high-fidelity simulator. The leg is represented in the simulator as 3D-printable file, and as such can be readily instantiated in reality. Provisional experiments in three distinct environments show the evolution of environment-specific leg structures that are both high-performing and notably different to those evolved in the other environments. This proof-of-concept represents an important step towards the environment-dependent optimisation of performance-critical components for a range of ubiquitous, standard, and already-capable robots that can carry out a wide variety of tasks.
]]></description>
<dc:subject>artificial-life genetic-programming robotics rather-interesting</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:721f870d8405/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1804.01660">
    <title>[1804.01660] The structure of evolved representations across different substrates for artificial intelligence</title>
    <dc:date>2021-11-04T10:18:17+00:00</dc:date>
    <link>https://arxiv.org/abs/1804.01660</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Artificial neural networks (ANNs), while exceptionally useful for classification, are vulnerable to misdirection. Small amounts of noise can significantly affect their ability to correctly complete a task. Instead of generalizing concepts, ANNs seem to focus on surface statistical regularities in a given task. Here we compare how recurrent artificial neural networks, long short-term memory units, and Markov Brains sense and remember their environments. We show that information in Markov Brains is localized and sparsely distributed, while the other neural network substrates "smear" information about the environment across all nodes, which makes them vulnerable to noise.
]]></description>
<dc:subject>artificial-life machine-learning markov-brains representation hey-I-know-this-guy to-write-about to-simulate</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:0278811305b5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:markov-brains"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hey-I-know-this-guy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1705.08756">
    <title>[1705.08756] Short and random: Modelling the effects of (proto-)neural elongations</title>
    <dc:date>2021-11-04T09:54:31+00:00</dc:date>
    <link>https://arxiv.org/abs/1705.08756</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[To understand how neurons and nervous systems first evolved, we need an account of the origins of neural elongations: Why did neural elongations (axons and dendrites) first originate, such that they could become the central component of both neurons and nervous systems? Two contrasting conceptual accounts provide different answers to this question. Braitenberg's vehicles provide the iconic illustration of the dominant input-output (IO) view. Here the basic role of neural elongations is to connect sensors to effectors, both situated at different positions within the body. For this function, neural elongations are thought of as comparatively long and specific connections, which require an articulated body involving substantial developmental processes to build. Internal coordination (IC) models stress a different function for early nervous systems. Here the coordination of activity across extended parts of a multicellular body is held central, in particular for the contractions of (muscle) tissue. An IC perspective allows the hypothesis that the earliest proto-neural elongations could have been functional even when they were initially simple short and random connections, as long as they enhanced the patterning of contractile activity across a multicellular surface. The present computational study provides a proof of concept that such short and random neural elongations can play this role. While an excitable epithelium can generate basic forms of patterning for small body-configurations, adding elongations allows such patterning to scale up to larger bodies. This result supports a new, more gradual evolutionary route towards the origins of the very first full neurons and nervous systems.
]]></description>
<dc:subject>artificial-life evolutionary-biology neural-networks rather-interesting simulation self-organization to-write-about to-simulate</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:21541c9410b0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:evolutionary-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:neural-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:self-organization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.openhumanitiespress.org/books/titles/machine-sensation/">
    <title>Open Humanities Press– Machine Sensation</title>
    <dc:date>2021-10-28T14:01:44+00:00</dc:date>
    <link>http://www.openhumanitiespress.org/books/titles/machine-sensation/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The title of this book is designed to provoke two lines of thought. First, there is the incredible effect of anthropomorphic machines on human imagination and culture. Anthropomorphism has a power to direct attention away from the alien nature of a technological object. Second, the philosophical study of the way machines sense and act in their worlds is essential for breaking free of the anthropomorphic effect. Tessa Leach argues that this is the foundation upon which we must base a study of technologies without undue emphasis on their human origins, which often means breaking free of the way that we group different objects under human-imposed naming systems. Object-oriented ontology is used as a way of of insisting upon the unhuman nature of technology while still acknowledging its immense power and significance in human life. Machine Sensation discusses such remarkable objects as natural user interfaces, artificial intelligence, and sex robots.

]]></description>
<dc:subject>philosophy-of-engineering philosophy object-oriented-ontology artificial-life to-read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:81bacf2072c0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy-of-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:object-oriented-ontology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.nature.com/articles/s41598-020-73380-x">
    <title>Sources of predictive information in dynamical neural networks | Scientific Reports</title>
    <dc:date>2021-07-23T12:55:44+00:00</dc:date>
    <link>https://www.nature.com/articles/s41598-020-73380-x</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Behavior involves the ongoing interaction between an organism and its environment. One of the prevailing theories of adaptive behavior is that organisms are constantly making predictions about their future environmental stimuli. However, how they acquire that predictive information is still poorly understood. Two complementary mechanisms have been proposed: predictions are generated from an agent’s internal model of the world or predictions are extracted directly from the environmental stimulus. In this work, we demonstrate that predictive information, measured using bivariate mutual information, cannot distinguish between these two kinds of systems. Furthermore, we show that predictive information cannot distinguish between organisms that are adapted to their environments and random dynamical systems exposed to the same environment. To understand the role of predictive information in adaptive behavior, we need to be able to identify where it is generated. To do this, we decompose information transfer across the different components of the organism-environment system and track the flow of information in the system over time. To validate the proposed framework, we examined it on a set of computational models of idealized agent-environment systems. Analysis of the systems revealed three key insights. First, predictive information, when sourced from the environment, can be reflected in any agent irrespective of its ability to perform a task. Second, predictive information, when sourced from the nervous system, requires special dynamics acquired during the process of adapting to the environment. Third, the magnitude of predictive information in a system can be different for the same task if the environmental structure changes.

]]></description>
<dc:subject>artificial-life machine-learning rather-interesting to-write-about to-simulate consider:symbolic-regression</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:4f39978dd85d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:symbolic-regression"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/ShprAlex/SproutLife">
    <title>ShprAlex/SproutLife: Evolving version of Conway’s Game of Life.</title>
    <dc:date>2021-07-22T09:34:20+00:00</dc:date>
    <link>https://github.com/ShprAlex/SproutLife</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[SproutLife simulates the evolution of complex life. It extends Conway’s Game of Life, which is famous for having lifelike behavior arise from simple rules. SproutLife takes this emergence a step further defining organisms that mutate and reproduce.

Run SproutLife on your desktop to see evolution in action. Observe the intricate geometric patterns that form, or dive deep to explore the data about how individual competition and collective fitness interact.

]]></description>
<dc:subject>artificial-life cellular-automata rather-interesting emergent-design to-try</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:38cf5a49221c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cellular-automata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:emergent-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-try"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://algorithmicbotany.org/papers/">
    <title>Algorithmic Botany: Publications</title>
    <dc:date>2021-05-20T11:23:22+00:00</dc:date>
    <link>http://algorithmicbotany.org/papers/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The following is a selection of the papers published by Dr. P. Prusinkiewicz and his students and colleagues. Report any problems to vlab@cpsc.ucalgary.ca.

]]></description>
<dc:subject>algorithms artificial-life generative-art rather-interesting bibliography to-write-about consider:animations</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:9255be81e8c4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:bibliography"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:animations"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://fronkonstin.com/2019/10/03/colonizing-franky/">
    <title>Colonizing Franky | Fronkonstin</title>
    <dc:date>2021-05-20T11:21:21+00:00</dc:date>
    <link>https://fronkonstin.com/2019/10/03/colonizing-franky/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[One of my favorite sites in the Internet is algorithmic botany . It’s always a source of inspiration for me. I recently discovered there the space colonization algorithm, concretely in this paper. Originally, the algorithm was developed to simulate leaf venation patterns as well as the branching structure of trees and it works by simulating the competition for space between growing veins (or branches). Given a initial set of attractor points (3.000 points in my case), and a initial node (also a point located randomly inside the picture) the algorithm performs the next steps iteratively:

]]></description>
<dc:subject>generative-art artificial-life swarms rather-interesting to-simulate</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:c97a917a8070/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:swarms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2103.04876">
    <title>[2103.04876] Scale invariant robot behavior with fractals</title>
    <dc:date>2021-05-18T22:28:15+00:00</dc:date>
    <link>https://arxiv.org/abs/2103.04876</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Robots deployed at orders of magnitude different size scales, and that retain the same desired behavior at any of those scales, would greatly expand the environments in which the robots could operate. However it is currently not known whether such robots exist, and, if they do, how to design them. Since self similar structures in nature often exhibit self similar behavior at different scales, we hypothesize that there may exist robot designs that have the same property. Here we demonstrate that this is indeed the case for some, but not all, modular soft robots: there are robot designs that exhibit a desired behavior at a small size scale, and if copies of that robot are attached together to realize the same design at higher scales, those larger robots exhibit similar behavior. We show how to find such designs in simulation using an evolutionary algorithm. Further, when fractal attachment is not assumed and attachment geometries must thus be evolved along with the design of the base robot unit, scale invariant behavior is not achieved, demonstrating that structural self similarity, when combined with appropriate designs, is a useful path to realizing scale invariant robot behavior. We validate our findings by demonstrating successful transferal of self similar structure and behavior to pneumatically-controlled soft robots. Finally, we show that biobots can spontaneously exhibit self similar attachment geometries, thereby suggesting that self similar behavior via self similar structure may be realizable across a wide range of robot platforms in future.
]]></description>
<dc:subject>biological-engineering biologically-inspired artificial-life absolutely-stunning hey-I-know-this-guy theoretical-biology self-organization autopoiesis to-write-about to-reframe</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:9d7c6be9339a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biological-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biologically-inspired"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:absolutely-stunning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hey-I-know-this-guy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:theoretical-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:self-organization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:autopoiesis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-reframe"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.mdpi.com/2227-9717/9/2/347/htm">
    <title>Processes | Free Full-Text | Parallel Multiset Rewriting Systems with Distorted Rules | HTML</title>
    <dc:date>2021-03-04T12:37:49+00:00</dc:date>
    <link>https://www.mdpi.com/2227-9717/9/2/347/htm</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Most of the parallel rewriting systems which model (or which are inspired by) natural/artificial phenomena consider fixed, a priori defined sets of string/multiset rewriting rules whose definitions do not change during the computation. Here we modify this paradigm by defining level-t distorted rules—rules for which during their applications one does not know the exact multiplicities of at most t∈N
 species of objects in their output (although one knows that such objects will appear at least once in the output upon the execution of this type of rules). Subsequently, we define parallel multiset rewriting systems with t-distorted computations and we study their computational capabilities when level-1 distorted catalytic promoted rules are used. We construct robust systems able to cope with the level-1 distortions and prove the computational universality of the model.
]]></description>
<dc:subject>to-read artificial-life membrane-computing rewriting-systems to-understand distributed-processing consider:ReQ-dynamics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:bcdde94456ef/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:membrane-computing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rewriting-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-understand"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:distributed-processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:ReQ-dynamics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.1101/081091v1?rss=1%252522">
    <title>Putting bandits into context: How function learning supports decision making | bioRxiv</title>
    <dc:date>2020-10-14T11:02:23+00:00</dc:date>
    <link>https://www.biorxiv.org/content/10.1101/081091v1?rss=1%252522</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We introduce the contextual multi-armed bandit task as a framework to investigate learning and decision making in uncertain environments. In this novel paradigm, participants repeatedly choose between multiple options in order to maximise their rewards. The options are described by a number of contextual features which are predictive of the rewards through initially unknown functions. From their experience with choosing options and observing the consequences of their decisions, participants can learn about the functional relation between contexts and rewards and improve their decision strategy over time. In three experiments, we find that participants’ behaviour is surprisingly adaptive to the learning environment. We model participants’ behaviour by context-blind (mean-tracking, Kalman filter) and contextual (Gaussian process regression parametrized with different kernels) learning approaches combined with different choice strategies. While participants generally learn about the context-reward functions, they tend to rely on a local learning strategy which generalizes previous experience only to highly similar instances. In a relatively simple task with binary features, they mostly combine this local learning with an “expected improvement” decision strategy which focuses on alternatives that are expected to improve the most upon a current favourite option. In a task with continuous features that are linearly related to the rewards, they combine local learning with a “upper confidence bound” decision strategy that more explicitly balances exploration and exploitation. Finally, in a difficult learning environment where the relation between features and rewards is non-linear, most participants learn locally as before, whereas others regress to more context-blind strategies.

]]></description>
<dc:subject>machine-learning artificial-life theoretical-biology rather-interesting to-write-about to-simulate consider:representation consider:metaheuristics-and-ontology</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:911ddb59839b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:theoretical-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:metaheuristics-and-ontology"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1706.03058">
    <title>[1706.03058] Thermodynamics of Evolutionary Games</title>
    <dc:date>2020-09-23T16:53:00+00:00</dc:date>
    <link>https://arxiv.org/abs/1706.03058</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[How cooperation can evolve between players is an unsolved problem of biology. Here we use Hamiltonian dynamics of models of the Ising type to describe populations of cooperating and defecting players to show that the equilibrium fraction of cooperators is given by the expectation value of a thermal observable akin to a magnetization. We apply the formalism to the Public Goods game with three players, and show that a phase transition between cooperation and defection occurs that is equivalent to a transition in one-dimensional Ising crystals with long-range interactions. We then investigate the effect of punishment on cooperation and find that punishment plays the role of a magnetic field that leads to an "alignment" between players, thus encouraging cooperation. We suggest that a thermal Hamiltonian picture of the evolution of cooperation can generate other insights about the dynamics of evolving groups by mining the rich literature of critical dynamics in low-dimensional spin systems.
]]></description>
<dc:subject>evolutionary-economics artificial-life hey-I-know-this-guy agent-based rather-interesting game-theory to-simulate to-write-about consider:visualization</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:b8f6d1ce23a8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:evolutionary-economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hey-I-know-this-guy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:agent-based"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:game-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:visualization"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1703.01209">
    <title>[1703.01209] Perovskite Quantum Organismoids</title>
    <dc:date>2020-07-13T13:54:38+00:00</dc:date>
    <link>https://arxiv.org/abs/1703.01209</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[A central characteristic of living beings is the ability to learn from and respond to their environment leading to habit formation and decision making1-3. This behavior, known as habituation, is universal among forms of life with a central nervous system, and interestingly observed even in single cellular organisms that do not possess a brain4-5. Here, we report the discovery of habituation based plasticity utilizing a perovskite quantum system by dynamical modulation of electron localization via reversible dopant incorporation. Microscopic mechanisms and pathways that enable this organismic collective charge-lattice interaction are elucidated by a combination of first-principles theory, synchrotron investigations, ab-initio dynamical simulations and in-situ environmental breathing studies. We implement a new learning algorithm inspired from the conductance relaxation behavior of perovskites that naturally incorporates habituation and demonstrate "learning to forget": a key feature of animal and human brains6. Most surprisingly, our results show that incorporating this elementary skill in learning dramatically boosts the capability of artificial cognitive systems.
]]></description>
<dc:subject>WTAF artificial-life biological-engineering rather-interesting to-understand materials-science machine-learning looking-to-see</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:c557d1585fc0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:WTAF"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biological-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-understand"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:materials-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:looking-to-see"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.mdpi.com/2075-1729/10/4/42">
    <title>Life | Free Full-Text | Defining Lyfe in the Universe: From Three Privileged Functions to Four Pillars</title>
    <dc:date>2020-06-18T10:28:04+00:00</dc:date>
    <link>https://www.mdpi.com/2075-1729/10/4/42</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Motivated by the need to paint a more general picture of what life is—and could be—with respect to the rest of the phenomena of the universe, we propose a new vocabulary for astrobiological research. Lyfe is defined as any system that fulfills all four processes of the living state, namely: dissipation, autocatalysis, homeostasis, and learning. Life is defined as the instance of lyfe that we are familiar with on Earth, one that uses a specific organometallic molecular toolbox to record information about its environment and achieve dynamical order by dissipating certain planetary disequilibria. This new classification system allows the astrobiological community to more clearly define the questions that propel their research—e.g., whether they are developing a historical narrative to explain the origin of life (on Earth), or a universal narrative for the emergence of lyfe, or whether they are seeking signs of life specifically, or lyfe at large across the universe. While the concept of “life as we don’t know it” is not new, the four pillars of lyfe offer a novel perspective on the living state that is indifferent to the particular components that might produce it. View Full-Text
]]></description>
<dc:subject>artificial-life cellular-automata rather-interesting to-write-about consider:individuation consider:interaction-grammars consider:examples</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:429f02a8186f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cellular-automata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:individuation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:interaction-grammars"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:examples"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.1101/120097v1?rss=1">
    <title>Fragmentation modes and the evolution of life cycles | bioRxiv</title>
    <dc:date>2020-06-14T12:42:05+00:00</dc:date>
    <link>https://www.biorxiv.org/content/10.1101/120097v1?rss=1</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Reproduction is a defining feature of living systems. Reproduction modes range from binary fission in bacteria to various modes of collective-level reproduction in multicellular organisms. However, the evolution of these modes and their adaptive significance is unclear. We develop a model in which groups arise from the division of single cells that do not separate, but stay together until the moment of group fragmentation. Fragmentation occurs via either complete or partial fission, resulting in a wide range of life cycles. By determining the relationship between life cycle and population growth rate, we define optimal fragmentation modes that have a surprisingly narrow class of solutions. Our model and results provide a framework for analysing the evolution of simple life cycles and for testing the adaptive significance of different modes of reproduction.

]]></description>
<dc:subject>theoretical-biology life-history artificial-life simulation rather-interesting to-write-about to-simulate consider:visualization</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:670e114b9954/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:theoretical-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:life-history"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:visualization"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2005.03742">
    <title>[2005.03742] Lenia and Expanded Universe</title>
    <dc:date>2020-05-21T12:03:00+00:00</dc:date>
    <link>https://arxiv.org/abs/2005.03742</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We report experimental extensions of Lenia, a continuous cellular automata family capable of producing lifelike self-organizing autonomous patterns. The rule of Lenia was generalized into higher dimensions, multiple kernels, and multiple channels. The final architecture approaches what can be seen as a recurrent convolutional neural network. Using semi-automatic search e.g. genetic algorithm, we discovered new phenomena like polyhedral symmetries, individuality, self-replication, emission, growth by ingestion, and saw the emergence of "virtual eukaryotes" that possess internal division of labor and type differentiation. We discuss the results in the contexts of biology, artificial life, and artificial intelligence.
]]></description>
<dc:subject>artificial-life cellular-automata rather-interesting rather-good to-write-about to-simulate consider:in-browser-sims</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:0415ebbd5a43/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cellular-automata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-good"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:in-browser-sims"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2003.14332">
    <title>[2003.14332] Artificial chemistry experiments with chemlambda, lambda calculus, interaction combinators</title>
    <dc:date>2020-05-19T23:48:15+00:00</dc:date>
    <link>https://arxiv.org/abs/2003.14332</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Given a graph rewrite system, a graph G is a quine graph if it has a non-void maximal collection of non-conflicting matches of left patterns of graphs rewrites, such that after the parallel application of the rewrites we obtain a graph isomorphic with G. Such graphs exhibit a metabolism, they can multiply or they can die, when reduced by a random rewriting algorithm. 
These are introductory notes to the pages of artificial chemistry experiments with chemlambda, lambda calculus or interaction combinators, available from the entry page this https URL . The experiments are bundled into pages, all of them based on a library of programs, on a database which contains hundreds of graphs and on a database of about 150 pages of text comments and a collection of more than 200 animations, most of them which can be re-done live, via the programs. There are links to public repositories of other contributors to these experiments, with versions of these programs in python, haskell, awk or javascript.
]]></description>
<dc:subject>artificial-life artificial-chemistry lambda-soup origin-of-life rather-interesting to-write-about self-organization</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:4bcfd4315916/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-chemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:lambda-soup"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:origin-of-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:self-organization"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://openai.com/blog/learning-dexterity/">
    <title>Learning Dexterity</title>
    <dc:date>2020-05-14T11:43:30+00:00</dc:date>
    <link>https://openai.com/blog/learning-dexterity/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[What surprised us
Tactile sensing is not necessary to manipulate real-world objects. Our robot receives only the locations of the five fingertips along with the position and orientation of the cube. Although the robot hand has touch sensors on its fingertips, we didn’t need to use them. Generally, we found better performance from using a limited set of sensors that could be modeled effectively in the simulator instead of a rich sensor set with values that were hard to model.
Randomizations developed for one object generalize to others with similar properties. After developing our system for the problem of manipulating a block, we printed an octagonal prism, trained a new policy using its shape, and attempted to manipulate it. Somewhat to our surprise, it achieved high performance using only the randomizations we had designed for the block. By contrast, a policy that manipulated a sphere could only achieve a few successes in a row, perhaps because we had not randomized any simulation parameters that model rolling behavior.
With physical robots, good systems engineering is as important as good algorithms. At one point, we noticed that one engineer consistently achieved much better performance than others when running the exact same policy. We later discovered that he had a faster laptop, which hid a timing bug that reduced performance. After the bug was fixed, performance improved for the rest of the team.
]]></description>
<dc:subject>robotics machine-learning situated-learning rather-interesting artificial-life algorithms to-write-about consider:simulation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:6d75c2000e2e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:situated-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:simulation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1801.06853">
    <title>[1801.06853] Basic Model of Purposeful Kinesis</title>
    <dc:date>2020-05-02T15:44:42+00:00</dc:date>
    <link>https://arxiv.org/abs/1801.06853</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The notions of taxis and kinesis are introduced and used to describe two types of behavior of an organism in non-uniform conditions: (i) Taxis means the guided movement to more favorable conditions; (ii) Kinesis is the non-directional change in space motion in response to the change of conditions. Migration and dispersal of animals has evolved under control of natural selection. In a simple formalisation, the strategy of dispersal should increase Darwinian fitness. We introduce new models of purposeful kinesis with diffusion coefficient dependent on fitness. The local and instant evaluation of Darwinian fitness is used, the reproduction coefficient. New models include one additional parameter, intensity of kinesis, and may be considered as the {\em minimal models of purposeful kinesis}. The properties of models are explored by a series of numerical experiments. It is demonstrated how kinesis could be beneficial for assimilation of patches of food or of periodic fluctuations. Kinesis based on local and instant estimations of fitness is not always beneficial: for species with the Allee effect it can delay invasion and spreading. It is proven that kinesis cannot modify stability of positive homogeneous steady states.
]]></description>
<dc:subject>theoretical-biology ethology rather-interesting animal-behavior agents statistics modeling to-simulate to-write-about artificial-life</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:bf23578e4341/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:theoretical-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:ethology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:animal-behavior"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:agents"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://openai.com/blog/emergent-tool-use/">
    <title>Emergent Tool Use from Multi-Agent Interaction</title>
    <dc:date>2020-02-19T12:12:06+00:00</dc:date>
    <link>https://openai.com/blog/emergent-tool-use/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We’ve observed agents discovering progressively more complex tool use while playing a simple game of hide-and-seek. Through training in our new simulated hide-and-seek environment, agents build a series of six distinct strategies and counterstrategies, some of which we did not know our environment supported. The self-supervised emergent complexity in this simple environment further suggests that multi-agent co-adaptation may one day produce extremely complex and intelligent behavior.
]]></description>
<dc:subject>agent-based artificial-life machine-learning collective-behavior looking-to-see rather-interesting to-write-about to-simulate consider:a-sufficiently-complex-environment</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:15e85b5e339f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:agent-based"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:collective-behavior"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:looking-to-see"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:a-sufficiently-complex-environment"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1809.02836">
    <title>[1809.02836] Context-Free Transductions with Neural Stacks</title>
    <dc:date>2020-02-16T13:02:06+00:00</dc:date>
    <link>https://arxiv.org/abs/1809.02836</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[This paper analyzes the behavior of stack-augmented recurrent neural network (RNN) models. Due to the architectural similarity between stack RNNs and pushdown transducers, we train stack RNN models on a number of tasks, including string reversal, context-free language modelling, and cumulative XOR evaluation. Examining the behavior of our networks, we show that stack-augmented RNNs can discover intuitive stack-based strategies for solving our tasks. However, stack RNNs are more difficult to train than classical architectures such as LSTMs. Rather than employ stack-based strategies, more complex networks often find approximate solutions by using the stack as unstructured memory.
]]></description>
<dc:subject>neural-networks artificial-life representation time-series automata to-write-about to-simulate unconventional-computing</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:4cf08b926f0e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:neural-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:time-series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:automata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:unconventional-computing"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://muircheartblog.wordpress.com/2019/06/07/does-art-compute/">
    <title>Does art compute? – Symptoms Of The Universe</title>
    <dc:date>2020-02-16T12:50:15+00:00</dc:date>
    <link>https://muircheartblog.wordpress.com/2019/06/07/does-art-compute/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[As Cory Simon explains so well in his “Voronoi cookies and the post office problem” post, the Voronoi algorithm is an easy-to-understand method in computational geometry, especially in two dimensions: take a point, join it up to its nearest neighbours, and get the perpendicular bisectors of those lines. The intersections of the bisectors define a Voronoi cell. If the points form an ordered mesh on the plane — as, for example, in the context of the atoms on a crystal plane in solid state physics — then the Voronoi cell is called a Wigner-Seitz unit cell. (As an undergrad, I didn’t realise that the Wigner-Seitz unit cells I studied in my solid state lectures were part of the much broader Voronoi class — another example of limiting thinking due to disciplinary boundaries.)

]]></description>
<dc:subject>unconventional-computing generative-art interdisciplinary artificial-life heavily-linked to-walk-from</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:49857fd3ab78/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:unconventional-computing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:interdisciplinary"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:heavily-linked"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-walk-from"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://softology.com.au/voc.htm">
    <title>Softology - Visions of Chaos</title>
    <dc:date>2019-11-03T19:59:29+00:00</dc:date>
    <link>https://softology.com.au/voc.htm</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Visions of Chaos is a professional high end software application for Windows. It is simple enough for people who do not understand the mathematics behind it, but advanced enough for fractal enthusiasts to tweak and customise to their needs. It is the most complete all in one application dealing with Chaos Theory available. Every mode is written to give the best possible quality output. There are thousands of sample files included to give you an idea of what Visions of Chaos is capable of. 
]]></description>
<dc:subject>cellular-automata Windows open-source artificial-life</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:85e6e4199c5c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cellular-automata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:Windows"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:open-source"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://softologyblog.wordpress.com/2018/03/31/more-explorations-with-multiple-neighborhood-cellular-automata/">
    <title>More explorations with Multiple Neighborhoods Cellular Automata | Softology's Blog</title>
    <dc:date>2019-11-03T18:51:17+00:00</dc:date>
    <link>https://softologyblog.wordpress.com/2018/03/31/more-explorations-with-multiple-neighborhood-cellular-automata/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[
]]></description>
<dc:subject>cellular-automata rather-interesting to-simulate to-explore artificial-life</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:321747fc6850/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cellular-automata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-explore"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1812.03601">
    <title>[1812.03601] A recipe for black box functors</title>
    <dc:date>2019-09-29T10:35:42+00:00</dc:date>
    <link>https://arxiv.org/abs/1812.03601</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The task of constructing compositional semantics for network-style diagrammatic languages, such as electrical circuits or chemical reaction networks, has been dubbed the black boxing problem, as it gives semantics that describes the properties of each network that can be observed externally, by composition, while discarding the internal structure. One way to solve these problems is to formalise the diagrams and their semantics using hypergraph categories, with semantic interpretation a hypergraph functor, called the black box functor, between them. Reviewing a principled method for constructing hypergraph categories and functors, known as decorated corelations, in this paper we construct a category of \emph{decorating data}, and show that the decorated corelations method is itself functorial, with a universal property characterised by a left Kan extension. We then argue that the category of decorating data is a good setting in which to construct any hypergraph functor, giving a new construction of Baez and Pollard's black box functor for reaction networks as an example.
]]></description>
<dc:subject>reaction-networks theoretical-biology hypergraphs rather-interesting to-write-about to-simulate category-theory artificial-life to-understand</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:cdf3a81c7b33/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:reaction-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:theoretical-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hypergraphs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:category-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-understand"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.mediamatic.net/en/page/10341/about-mediamatic">
    <title>About Mediamatic - Mediamatic</title>
    <dc:date>2019-09-25T10:20:10+00:00</dc:date>
    <link>https://www.mediamatic.net/en/page/10341/about-mediamatic</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Mediamatic is an art centre dedicated to new developments in the arts since 1983. We organize lectures, workshops and art projects, focusing on nature, biotechnology and art+science in a strong international network.

]]></description>
<dc:subject>artificial-life collective didn't-know-they-were-still-around</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:430d7785e049/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:collective"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:didn't-know-they-were-still-around"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1810.01999">
    <title>[1810.01999] Jamming by growth</title>
    <dc:date>2019-09-08T19:39:44+00:00</dc:date>
    <link>https://arxiv.org/abs/1810.01999</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Growth in confined spaces can drive cellular populations through a jamming transition from a fluid-like state to a solid-like state. Experiments have found that jammed budding yeast populations can build up extreme compressive pressures (over 1MPa), which in turn feed back onto cellular physiology by slowing or even stalling cell growth. Using extensive numerical simulations, we investigate how this feedback impacts the mechanical properties of model jammed cellular populations. We find that feedback directs growth toward poorly-coordinated regions, resulting in an excess number of cell-cell contacts that rigidify cell packings. Cell packings posses anomalously large shear and bulk moduli that depend sensitively on the strength of feedback. These results demonstrate that mechanical feedback on the single-cell level is a simple mechanism by which living systems can tune their population-level mechanical properties.
]]></description>
<dc:subject>rather-interesting granular-materials artificial-life simulation to-simulate resource-limitation theoretical-biology packing</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:d3f9f8b14c27/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:granular-materials"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:resource-limitation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:theoretical-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:packing"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/0810.3179">
    <title>[0810.3179] The Enlightened Game of Life</title>
    <dc:date>2019-09-08T12:47:08+00:00</dc:date>
    <link>https://arxiv.org/abs/0810.3179</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We investigate a special class of cellular automata (CA) evolving in a environment filled by an electromagnetic wave. The rules of the Conway's Game of Life are modified to account for the ability to retrieve life-sustenance from the field energy. Light-induced self-structuring and self-healing abilities and various dynamic phases are displayed by the CA. Photo-driven genetic selection and the nonlinear feedback of the CA on the electromagnetic field are included in the model, and there are evidences of self-organized light-localization processes. The evolution of the electromagnetic field is based on the Finite Difference Time Domain (FDTD) approach. Applications are envisaged in evolutionary biology, artificial life, DNA replication, swarming, optical tweezing and field-driven soft-matter.
]]></description>
<dc:subject>cellular-automata mathematical-diffraction rather-odd rather-interesting artificial-life to-understand to-write-about to-amplify</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:d7abbb4be4e8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cellular-automata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:mathematical-diffraction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-odd"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-understand"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-amplify"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1903.01373">
    <title>[1903.01373] α-Rank: Multi-Agent Evaluation by Evolution</title>
    <dc:date>2019-04-24T13:56:04+00:00</dc:date>
    <link>https://arxiv.org/abs/1903.01373</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We introduce {\alpha}-Rank, a principled evolutionary dynamics methodology for the evaluation and ranking of agents in large-scale multi-agent interactions, grounded in a novel dynamical game-theoretic solution concept called Markov-Conley chains (MCCs). The approach leverages continuous- and discrete-time evolutionary dynamical systems applied to empirical games, and scales tractably in the number of agents, the type of interactions, and the type of empirical games (symmetric and asymmetric). Current models are fundamentally limited in one or more of these dimensions and are not guaranteed to converge to the desired game-theoretic solution concept (typically the Nash equilibrium). {\alpha}-Rank provides a ranking over the set of agents under evaluation and provides insights into their strengths, weaknesses, and long-term dynamics. This is a consequence of the links we establish to the MCC solution concept when the underlying evolutionary model's ranking-intensity parameter, {\alpha}, is chosen to be large, which exactly forms the basis of {\alpha}-Rank. In contrast to the Nash equilibrium, which is a static concept based on fixed points, MCCs are a dynamical solution concept based on the Markov chain formalism, Conley's Fundamental Theorem of Dynamical Systems, and the core ingredients of dynamical systems: fixed points, recurrent sets, periodic orbits, and limit cycles. {\alpha}-Rank runs in polynomial time with respect to the total number of pure strategy profiles, whereas computing a Nash equilibrium for a general-sum game is known to be intractable. We introduce proofs that not only provide a unifying perspective of existing continuous- and discrete-time evolutionary evaluation models, but also reveal the formal underpinnings of the {\alpha}-Rank methodology. We empirically validate the method in several domains including AlphaGo, AlphaZero, MuJoCo Soccer, and Poker.
]]></description>
<dc:subject>collective-behavior performance-measure rather-interesting teams game-theory credit-assignment algorithms machine-learning to-understand artificial-life</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:efde9b976c54/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:collective-behavior"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:performance-measure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:teams"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:game-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:credit-assignment"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-understand"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.mitpressjournals.org/doi/abs/10.1162/artl_a_00263">
    <title>The Emergence of Canalization and Evolvability in an Open-Ended, Interactive Evolutionary System | Artificial Life | MIT Press Journals</title>
    <dc:date>2019-03-03T13:32:28+00:00</dc:date>
    <link>https://www.mitpressjournals.org/doi/abs/10.1162/artl_a_00263</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Many believe that an essential component for the discovery of the tremendous diversity in natural organisms was the evolution of evolvability, whereby evolution speeds up its ability to innovate by generating a more adaptive pool of offspring. One hypothesized mechanism for evolvability is developmental canalization, wherein certain dimensions of variation become more likely to be traversed and others are prevented from being explored (e.g., offspring tend to have similar-size legs, and mutations affect the length of both legs, not each leg individually). While ubiquitous in nature, canalization is rarely reported in computational simulations of evolution, which deprives us of in silico examples of canalization to study and raises the question of which conditions give rise to this form of evolvability. Answering this question would shed light on why such evolvability emerged naturally, and it could accelerate engineering efforts to harness evolution to solve important engineering challenges. In this article, we reveal a unique system in which canalization did emerge in computational evolution. We document that genomes entrench certain dimensions of variation that were frequently explored during their evolutionary history. The genetic representation of these organisms also evolved to be more modular and hierarchical than expected by chance, and we show that these organizational properties correlate with increased fitness. Interestingly, the type of computational evolutionary experiment that produced this evolvability was very different from traditional digital evolution in that there was no objective, suggesting that open-ended, divergent evolutionary processes may be necessary for the evolution of evolvability.

]]></description>
<dc:subject>artificial-life contingency evolution theoretical-biology evolvability to-write-about hey-I-know-this-guy</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:ac982b62ae0d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:contingency"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:evolution"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:theoretical-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:evolvability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hey-I-know-this-guy"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1812.05433">
    <title>[1812.05433] Lenia - Biology of Artificial Life</title>
    <dc:date>2019-02-13T11:27:17+00:00</dc:date>
    <link>https://arxiv.org/abs/1812.05433</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We report a new model of artificial life called Lenia (from Latin lenis "smooth"), a two-dimensional cellular automaton with continuous space-time-state and generalized local rule. Computer simulations show that Lenia supports a great diversity of complex autonomous patterns or "lifeforms" bearing resemblance to real-world microscopic organisms. More than 400 species in 18 families have been identified, many discovered via interactive evolutionary computation. They differ from other cellular automata patterns in being geometric, metameric, fuzzy, resilient, adaptive, and rule-generic. 
We present basic observations of the model regarding the properties of space-time and basic settings. We provide a board survey of the lifeforms, categorize them into a hierarchical taxonomy, and map their distribution in the parameter hyperspace. We describe their morphological structures and behavioral dynamics, propose possible mechanisms of their self-propulsion, self-organization and plasticity. Finally, we discuss how the study of Lenia would be related to biology, artificial life, and artificial intelligence.]]></description>
<dc:subject>artificial-life representation cellular-automata rather-interesting to-write-about to-implement consider:simulation consider:abstraction</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:12ba8be79fac/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cellular-automata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-implement"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:abstraction"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://peerj.com/preprints/27122/">
    <title>What else is in an evolved name? Exploring evolvable specificity with SignalGP [PeerJ Preprints]</title>
    <dc:date>2019-01-26T11:40:02+00:00</dc:date>
    <link>https://peerj.com/preprints/27122/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Tags are evolvable labels that provide genetic programs a flexible mechanism for specification. Tags are used to label and refer to programmatic elements, such as functions or jump targets. However, tags differ from traditional, more rigid methods for handling labeling because they allow for inexact references; that is, a referring tag need not exactly match its referent. Here, we explore how adjusting the threshold for how what qualifies as a match affects adaptive evolution. Further, we propose broadened applications of tags in the context of a genetic programming (GP) technique called SignalGP. SignalGP gives evolution direct access to the event-driven paradigm. Program modules in SignalGP are tagged and can be triggered by signals (with matching tags) from the environment, from other agents, or due to internal regulation. Specifically, we propose to extend this tag based system to: (1) provide more fine-grained control over module execution and regulation (e.g., promotion and repression) akin to natural gene regulatory networks, (2) employ a mosaic of GP representations within a single program, and (3) facilitate major evolutionary transitions in individuality (i.e., allow hierarchical program organization to evolve de novo).

]]></description>
<dc:subject>artificial-life genetic-programming representation hey-I-know-this-guy the-mangle-in-practice to-examine consider:`ReQ`</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:b1a1557763cd/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hey-I-know-this-guy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:the-mangle-in-practice"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-examine"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:`ReQ`"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1811.04960">
    <title>[1811.04960] Molecular computers</title>
    <dc:date>2019-01-25T11:24:43+00:00</dc:date>
    <link>https://arxiv.org/abs/1811.04960</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We propose the chemlambda artificial chemistry, whose behavior strongly suggests that real molecules which embed Interaction Nets patterns and real chemical reactions which resemble Interaction Nets graph rewrites could be a realistic path towards molecular computers, in the sense explained in the article.
]]></description>
<dc:subject>artificial-chemistry graph-rewriting artificial-life rather-interesting to-simulate to-write-about concurrency consider:the-algorithm-loop-dichotomy consider:the-edges-of-things</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:f0453ce9d016/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-chemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:graph-rewriting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:concurrency"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:the-algorithm-loop-dichotomy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:the-edges-of-things"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1807.05283">
    <title>[1807.05283] When Are Two Gossips the Same? Types of Communication in Epistemic Gossip Protocols</title>
    <dc:date>2018-12-31T13:09:04+00:00</dc:date>
    <link>https://arxiv.org/abs/1807.05283</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We provide an in-depth study of the knowledge-theoretic aspects of communication in so-called gossip protocols. Pairs of agents communicate by means of calls in order to spread information---so-called secrets---within the group. Depending on the nature of such calls knowledge spreads in different ways within the group. Systematizing existing literature, we identify 18 different types of communication, and model their epistemic effects through corresponding indistinguishability relations. We then provide a classification of these relations and show its usefulness for an epistemic analysis in presence of different communication types. Finally, we explain how to formalise the assumption that the agents have common knowledge of a distributed epistemic gossip protocol.
]]></description>
<dc:subject>agent-based graph-theory communication artificial-life collective-behavior simulation define-your-terms rather-interesting to-simulate</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:2bb94223686b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:agent-based"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:graph-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:communication"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:collective-behavior"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:define-your-terms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1711.09442">
    <title>[1711.09442] Quantum Artificial Life in an IBM Quantum Computer</title>
    <dc:date>2018-12-26T11:36:05+00:00</dc:date>
    <link>https://arxiv.org/abs/1711.09442</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We present the first experimental realization of a quantum artificial life algorithm in a quantum computer. The quantum biomimetic protocol encodes tailored quantum behaviors belonging to living systems, namely, self-replication, mutation, interaction between individuals, and death, into the cloud quantum computer IBM ibmqx4. In this experiment, entanglement spreads throughout generations of individuals, where genuine quantum information features are inherited through genealogical networks. As a pioneering proof-of-principle, experimental data fits the ideal model with accuracy. Thereafter, these and other models of quantum artificial life, for which no classical device may predict its quantum supremacy evolution, can be further explored in novel generations of quantum computers. Quantum biomimetics, quantum machine learning, and quantum artificial intelligence will move forward hand in hand through more elaborate levels of quantum complexity.]]></description>
<dc:subject>artificial-life simulation quantum uh-huh</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:1925a5ff7dc9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:quantum"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:uh-huh"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.sns.ias.edu/pitp2/2012files/Probabilistic_Logics.pdf">
    <title>[PDF] PROBABILISTIC LOGICS AND THE SYNTHESIS OF RELIABLE ORGANISMS FROM UNRELIABLE COMPONENTS By J. von Neumann</title>
    <dc:date>2018-12-09T11:23:00+00:00</dc:date>
    <link>http://www.sns.ias.edu/pitp2/2012files/Probabilistic_Logics.pdf</link>
    <dc:creator>Vaguery</dc:creator><dc:subject>foundational-work artificial-life representation numerical-methods automata ReQ to-write-about to-cite</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:05756a6eeadf/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:foundational-work"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:numerical-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:automata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:ReQ"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-cite"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://peerj.com/preprints/27315/">
    <title>Ecological theory provides insights about evolutionary computation [PeerJ Preprints]</title>
    <dc:date>2018-11-04T14:19:10+00:00</dc:date>
    <link>https://peerj.com/preprints/27315/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Evolutionary algorithms often incorporate ecological concepts to help maintain diverse populations and drive continued innovation. However, while there is strong evidence for the value of ecological dynamics, a lack of overarching theoretical framework renders the precise mechanisms behind these results unclear. These gaps in our understanding make it challenging to predict which approaches will be most appropriate for a given problem. Biologists have been developing ecological theory for decades, but the resulting body of work has yet to be translated into an evolutionary computation context. This paper lays the groundwork for such a translation by applying ecological theory to three different selection mechanisms in evolutionary computation: fitness sharing, lexicase selection, and Eco-EA. First, we use ecological ideas to establish a framework that clarifies how these selection schemes are alike and how they differ. We then build upon this framework by using metrics from ecology to gather empirical data about the underlying differences in the population dynamics that these approaches produce. Specifically, we measure interaction networks and phylogenetic diversity within the population to explore long-term stable coexistence. Notably, we find that selection methods affect phylogenetic diversity differently than phenotypic diversity. These results can inform parameter selection, choice of selection scheme, and the development of new selection schemes.

]]></description>
<dc:subject>evolutionary-algorithms selection looking-to-see rather-interesting hey-I-know-these-folks artificial-life feature-construction community-formation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:6fd63e992eb7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:evolutionary-algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:selection"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:looking-to-see"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hey-I-know-these-folks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:feature-construction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:community-formation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1808.05875">
    <title>[1808.05875] Co-evolution of nodes and links: diversity driven coexistence in cyclic competition of three species</title>
    <dc:date>2018-08-20T11:35:14+00:00</dc:date>
    <link>https://arxiv.org/abs/1808.05875</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[When three species compete cyclically in a well-mixed, stochastic system of N individuals, extinction is known to typically occur at times scaling as the system size N. This happens, for example, in rock-paper-scissors games or conserved Lotka-Volterra models in which every pair of individuals can interact on a complete graph. Here we show that if the competing individuals also have a "social temperament" to be either introverted or extroverted, leading them to cut or add links respectively, then long-living state in which all species coexist can occur when both introverts and extroverts are present. These states are non-equilibrium quasi-steady states, maintained by a subtle balance between species competition and network dynamcis. Remarkably, much of the phenomena is embodied in a mean-field description. However, an intuitive understanding of why diversity stabilizes the co-evolving node and link dynamics remains an open issue.]]></description>
<dc:subject>coevolution theoretical-biology rather-interesting population-biology social-norms to-write-about to-simulate artificial-life it's-more-complicated-than-you-think complexology agent-based</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:f60dbf50ce32/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:coevolution"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:theoretical-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:population-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:social-norms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:it's-more-complicated-than-you-think"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:complexology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:agent-based"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.tandfonline.com/doi/abs/10.1080/17445760.2017.1410819?journalCode=gpaa20">
    <title>Model of the motion of agents with memory based on the cellular automaton: International Journal of Parallel, Emergent and Distributed Systems: Vol 33, No 3</title>
    <dc:date>2018-06-30T11:24:21+00:00</dc:date>
    <link>https://www.tandfonline.com/doi/abs/10.1080/17445760.2017.1410819?journalCode=gpaa20</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The article is devoted to the construction of the motion model for agents with memory. Agents can be interpreted, for example, as mobile robots or soldiers. Agents move on the landscape consisting of squares with different passability. The model is based on the cellular automaton with one common to all agents layer corresponding to the landscape and many agent-specific layers corresponding to an agent’s memory. Methods for the random landscape generation are developed. The dependence between configuration entropy of the landscape, efficiency of the path-finding algorithm based on the cellular automaton was found. Also, the dependence of the average speed of the agents’ motion on the landscape configuration entropy was shown.

]]></description>
<dc:subject>cannot-read what-was-that-dude's-name-at-Shippenssburg? cellular-automata artificial-life</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:b930829a6d81/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cannot-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:what-was-that-dude's-name-at-Shippenssburg?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cellular-automata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://deepmind.com/blog/grid-cells/">
    <title>Navigating with grid-like representations in artificial agents | DeepMind</title>
    <dc:date>2018-05-26T13:59:40+00:00</dc:date>
    <link>https://deepmind.com/blog/grid-cells/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Most animals, including humans, are able to flexibly navigate the world they live in – exploring new areas, returning quickly to remembered places, and taking shortcuts. Indeed, these abilities feel so easy and natural that it is not immediately obvious how complex the underlying processes really are. In contrast, spatial navigation remains a substantial challenge for artificial agents whose abilities are far outstripped by those of mammals.

]]></description>
<dc:subject>ethology experiment artificial-life neural-networks to-write-about consider:nudge consider:pattern-libraries</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:5222a281ced1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:ethology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:experiment"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:neural-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:nudge"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:pattern-libraries"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1803.05859v3">
    <title>[1803.05859v3] Neural Network Quine</title>
    <dc:date>2018-05-16T12:10:37+00:00</dc:date>
    <link>https://arxiv.org/abs/1803.05859v3</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Self-replication is a key aspect of biological life that has been largely overlooked in Artificial Intelligence systems. Here we describe how to build and train self-replicating neural networks. The network replicates itself by learning to output its own weights. The network is designed using a loss function that can be optimized with either gradient-based or non-gradient-based methods. We also describe a method we call regeneration to train the network without explicit optimization, by injecting the network with predictions of its own parameters. The best solution for a self-replicating network was found by alternating between regeneration and optimization steps. Finally, we describe a design for a self-replicating neural network that can solve an auxiliary task such as MNIST image classification. We observe that there is a trade-off between the network's ability to classify images and its ability to replicate, but training is biased towards increasing its specialization at image classification at the expense of replication. This is analogous to the trade-off between reproduction and other tasks observed in nature. We suggest that a self-replication mechanism for artificial intelligence is useful because it introduces the possibility of continual improvement through natural selection.
]]></description>
<dc:subject>artificial-life machine-learning quines rather-interesting to-write-about hey-I-know-this-guy</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:89488f560df5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:quines"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hey-I-know-this-guy"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1711.08988">
    <title>[1711.08988] Exponential growth for self-reproduction in a catalytic reaction network: relevance of a minority molecular species and crowdedness</title>
    <dc:date>2018-02-22T00:18:19+00:00</dc:date>
    <link>https://arxiv.org/abs/1711.08988</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Explanation of exponential growth in self-reproduction is an important step toward elucidation of the origins of life because optimization of the growth potential across rounds of selection is necessary for Darwinian evolution. To produce another copy with approximately the same composition, the exponential growth rates for all components have to be equal. How such balanced growth is achieved, however, is not a trivial question, because this kind of growth requires orchestrated replication of the components in stochastic and nonlinear catalytic reactions. By considering a mutually catalyzing reaction in two- and three-dimensional lattices, as represented by a cellular automaton model, we show that self-reproduction with exponential growth is possible only when the replication and degradation of one molecular species is much slower than those of the others, i.e., when there is a minority molecule. Here, the synergetic effect of molecular discreteness and crowding is necessary to produce the exponential growth. Otherwise, the growth curves show superexponential growth because of nonlinearity of the catalytic reactions or subexponential growth due to replication inhibition by overcrowding of molecules. Our study emphasizes that the minority molecular species in a catalytic reaction network is necessary to acquire evolvability at the primitive stage of life.]]></description>
<dc:subject>autocatalysis artificial-life origin-of-life reaction-networks self-organization biochemistry simulation rather-interesting to-write-about to-simulate</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:58e22fb5fef7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:autocatalysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:origin-of-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:reaction-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:self-organization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1508.06655">
    <title>[1508.06655] Tapping Into the Wells of Social Energy: A Case Study Based on Falls Identification</title>
    <dc:date>2018-01-26T12:59:04+00:00</dc:date>
    <link>https://arxiv.org/abs/1508.06655</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Are purely technological solutions the best answer we can get to the shortcomings our organizations are often experiencing today? The results we gathered in this work lead us to giving a negative answer to such question. Science and technology are powerful boosters, though when they are applied to the "local, static organization of an obsolete yesterday" they fail to translate in the solutions we need to our problems. Our stance here is that those boosters should be applied to novel, distributed, and dynamic models able to allow us to escape from the local minima our societies are currently locked in. One such model is simulated in this paper to demonstrate how it may be possible to tap into the vast basins of social energy of our human societies to realize ubiquitous computing sociotechnical services for the identification and timely response to falls.
]]></description>
<dc:subject>social-networks simulation artificial-life social-dynamics to-write-about to-simulate performance-measure exploration-and-exploitation metaphor philosophy-of-engineering</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:4779c187a536/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:social-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:social-dynamics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:performance-measure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:exploration-and-exploitation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:metaphor"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy-of-engineering"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1709.08800">
    <title>[1709.08800] TuringMobile: A Turing Machine of Oblivious Mobile Robots with Limited Visibility and its Applications</title>
    <dc:date>2018-01-26T12:29:28+00:00</dc:date>
    <link>https://arxiv.org/abs/1709.08800</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In this paper we investigate the computational power of a set of mobile robots with limited visibility. At each iteration, a robot takes a snapshot of its surroundings, uses the snapshot to compute a destination point, and it moves toward its destination. Each robot is punctiform and memoryless, it operates in ℝm, it has a local reference system independent of the other robots' ones, and is activated asynchronously by an adversarial scheduler. Moreover, robots are non-rigid, in that they may be stopped by the scheduler at each move before reaching their destination (but are guaranteed to travel at least a fixed unknown distance before being stopped). 
We show that despite these strong limitations, it is possible to arrange 3m+3k of these weak entities in ℝm to simulate the behavior of a stronger robot that is rigid (i.e., it always reaches its destination) and is endowed with k registers of persistent memory, each of which can store a real number. We call this arrangement a TuringMobile. In its simplest form, a TuringMobile consisting of only three robots can travel in the plane and store and update a single real number. We also prove that this task is impossible with fewer than three robots. 
Among the applications of the TuringMobile, we focused on Near-Gathering (all robots have to gather in a small-enough disk) and Pattern Formation (of which Gathering is a special case) with limited visibility. Interestingly, our investigation implies that both problems are solvable in Euclidean spaces of any dimension, even if the visibility graph of the robots is initially disconnected, provided that a small amount of these robots are arranged to form a TuringMobile. In the special case of the plane, a basic TuringMobile of only three robots is sufficient.]]></description>
<dc:subject>artificial-life swarms rather-interesting computer-science computational-complexity to-simulate to-write-about emergent-design distributed-processing nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:e7ac1be6472f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:swarms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:computer-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:computational-complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:emergent-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:distributed-processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1711.07387">
    <title>[1711.07387] How morphological development can guide evolution</title>
    <dc:date>2017-12-03T13:24:47+00:00</dc:date>
    <link>https://arxiv.org/abs/1711.07387</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Organisms result from multiple adaptive processes occurring and interacting at different time scales. One such interaction is that between development and evolution. In modeling studies, it has been shown that development sweeps over a series of traits in a single agent, and sometimes exposes promising static traits. Subsequent evolution can then canalize these rare traits. Thus, development can, under the right conditions, increase evolvability. Here, we report on a previously unknown phenomenon when embodied agents are allowed to develop and evolve: Evolution discovers body plans which are robust to control changes, these body plans become genetically assimilated, yet controllers for these agents are not assimilated. This allows evolution to continue climbing fitness gradients by tinkering with the developmental programs for controllers within these permissive body plans. This exposes a previously unknown detail about the Baldwin effect: instead of all useful traits becoming genetically assimilated, only phenotypic traits that render the agent robust to changes in other traits become assimilated. We refer to this phenomenon as differential canalization. This finding also has important implications for the evolutionary design of artificial and embodied agents such as robots: robots that are robust to internal changes in their controllers may also be robust to external changes in their environment, such as transferal from simulation to reality, or deployment in novel environments.
]]></description>
<dc:subject>artificial-life evolved-devo developmental-biology representation rather-interesting genetic-programming hey-I-know-this-guy</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:d6e01bd87f2e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:evolved-devo"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:developmental-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hey-I-know-this-guy"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1708.06458v1">
    <title>[1708.06458v1] (Tissue) P Systems with Vesicles of Multisets</title>
    <dc:date>2017-11-06T11:37:28+00:00</dc:date>
    <link>https://arxiv.org/abs/1708.06458v1</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We consider tissue P systems working on vesicles of multisets with the very simple operations of insertion, deletion, and substitution of single objects. With the whole multiset being enclosed in a vesicle, sending it to a target cell can be indicated in those simple rules working on the multiset. As derivation modes we consider the sequential mode, where exactly one rule is applied in a derivation step, and the set maximal mode, where in each derivation step a non-extendable set of rules is applied. With the set maximal mode, computational completeness can already be obtained with tissue P systems having a tree structure, whereas tissue P systems even with an arbitrary communication structure are not computationally complete when working in the sequential mode. Adding polarizations (-1, 0, 1 are sufficient) allows for obtaining computational completeness even for tissue P systems working in the sequential mode.]]></description>
<dc:subject>P-systems formal-languages artificial-life to-write-about to-simulate</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:d2fb442d128b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:P-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:formal-languages"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1705.00094">
    <title>[1705.00094] The Impact of Coevolution and Abstention on the Emergence of Cooperation</title>
    <dc:date>2017-10-21T12:46:48+00:00</dc:date>
    <link>https://arxiv.org/abs/1705.00094</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[This paper explores the Coevolutionary Optional Prisoner's Dilemma (COPD) game, which is a simple model to coevolve game strategy and link weights of agents playing the Optional Prisoner's Dilemma game. We consider a population of agents placed in a lattice grid with boundary conditions. A number of Monte Carlo simulations are performed to investigate the impacts of the COPD game on the emergence of cooperation. Results show that the coevolutionary rules enable cooperators to survive and even dominate, with the presence of abstainers in the population playing a key role in the protection of cooperators against exploitation from defectors. We observe that in adverse conditions such as when the initial population of abstainers is too scarce/abundant, or when the temptation to defect is very high, cooperation has no chance of emerging. However, when the simple coevolutionary rules are applied, cooperators flourish.]]></description>
<dc:subject>IPD agent-based evolutionary-economics coevolution community-formation simulation artificial-life game-theory to-write-about to-do</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:5fb784208ab8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:IPD"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:agent-based"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:evolutionary-economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:coevolution"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:community-formation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:game-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-do"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.researchgate.net/publication/220063885_A_parallel_approach_to_the_Eulerian_cycle_problem">
    <title>A parallel approach to the Eulerian cycle problem (PDF Download Available)</title>
    <dc:date>2017-09-19T11:26:58+00:00</dc:date>
    <link>https://www.researchgate.net/publication/220063885_A_parallel_approach_to_the_Eulerian_cycle_problem</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[A novel parallel approach for constructing Eulerian cycles of a given graph is presented. The proposed approach constitutes a combination of genetic algorithms and artificial neural networks. By tackling the Eulerian cycle problem as a constraint optimization problem, Eulerian cycle existence is determined, and either Eulerian cycles (if they exist) or paths encompassing the greatest possible number of edges (maximal traversal of edges, with edges traversed no more than once) are consistently constructed. Apart from being of theoretical interest, Eulerian cycle existence and construction have recently found significant applications in such areas of VLSI circuit design, RAM fault detection, and CPU access. © 2002 Wiley Periodicals, Inc.
]]></description>
<dc:subject>algorithms collective-intelligence emergent-design artificial-life to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:25dcc918b2c3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:collective-intelligence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:emergent-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://comdig.unam.mx/2017/08/31/self-replicators-emerge-from-a-self-organizing-prebiotic-computer-world/">
    <title>Self-Replicators Emerge from a Self-Organizing Prebiotic Computer World – Complexity Digest</title>
    <dc:date>2017-09-02T12:55:35+00:00</dc:date>
    <link>https://comdig.unam.mx/2017/08/31/self-replicators-emerge-from-a-self-organizing-prebiotic-computer-world/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Amoeba, a computer platform inspired by the Tierra system, is designed to study the generation of self-replicating sequences of machine operations (opcodes) from a prebiotic world initially populated by randomly selected opcodes. Point mutations drive opcode sequences to become more fit as they compete for memory and CPU time. Significant features of the Amoeba system include the lack of artificial encapsulation (there is no write protection) and a computationally universal opcode basis set. Amoeba now includes two additional features: pattern-based addressing and injecting entropy into the system. It was previously thought such changes would make it highly unlikely that an ancestral replicator could emerge from a fortuitous combination of randomly selected opcodes. Instead, Amoeba shows a far richer emergence, exhibiting a self-organization phase followed by the emergence of self-replicators. First, the opcode basis set becomes biased. Second, short opcode building blocks are propagated throughout memory space. Finally, prebiotic building blocks can combine to form self-replicators. Self-organization is quantified by measuring the evolution of opcode frequencies, the size distribution of sequences, and the mutual information of opcode pairs.

]]></description>
<dc:subject>to-read artificial-life abiogenesis theoretical-biology simulation emergence</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:3c263527cdd1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:abiogenesis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:theoretical-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:emergence"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1707.06631">
    <title>[1707.06631] Two Results on Slime Mold Computations</title>
    <dc:date>2017-08-07T11:31:24+00:00</dc:date>
    <link>https://arxiv.org/abs/1707.06631</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In this paper, we present two results on slime mold computations. The first one treats a biologically-grounded model, originally proposed by biologists analyzing the behavior of the slime mold Physarum polycephalum. This primitive organism was empirically shown by Nakagaki et al. to solve shortest path problems in wet-lab experiments (Nature'00). We show that the proposed simple mathematical model actually generalizes to a much wider class of problems, namely undirected linear programs with a non-negative cost vector. 
For our second result, we consider the discretization of a biologically-inspired model. This model is a directed variant of the biologically-grounded one and was never claimed to describe the behavior of a biological system. Straszak and Vishnoi showed that it can ϵ-approximately solve flow problems (SODA'16) and even general linear programs with positive cost vector (ITCS'16) within a finite number of steps. We give a refined convergence analysis that improves the dependence on ϵ from polynomial to logarithmic and simultaneously allows to choose a step size that is independent of ϵ. Furthermore, we show that the dynamics can be initialized with a more general set of (infeasible) starting points.
]]></description>
<dc:subject>collective-intelligence emergent-design artificial-life operations-research performance-measure to-write-about to-simulate</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:b0a7dae2bbd4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:collective-intelligence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:emergent-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:operations-research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:performance-measure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1512.02832">
    <title>[1512.02832] Connectivity Preserving Network Transformers</title>
    <dc:date>2017-04-29T20:47:29+00:00</dc:date>
    <link>https://arxiv.org/abs/1512.02832</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The Population Protocol model is a distributed model that concerns systems of very weak computational entities that cannot control the way they interact. The model of Network Constructors is a variant of Population Protocols capable of (algorithmically) constructing abstract networks. Both models are characterized by a fundamental inability to terminate. In this work, we investigate the minimal strengthenings of the latter that could overcome this inability. Our main conclusion is that initial connectivity of the communication topology combined with the ability of the protocol to transform the communication topology plus a few other local and realistic assumptions are sufficient to guarantee not only termination but also the maximum computational power that one can hope for in this family of models. The technique is to transform any initial connected topology to a less symmetric and detectable topology without ever breaking its connectivity during the transformation. The target topology of all of our transformers is the spanning line and we call Terminating Line Transformation the corresponding problem. We first study the case in which there is a pre-elected unique leader and give a time-optimal protocol for Terminating Line Transformation. We then prove that dropping the leader without additional assumptions leads to a strong impossibility result. In an attempt to overcome this, we equip the nodes with the ability to tell, during their pairwise interactions, whether they have at least one neighbor in common. Interestingly, it turns out that this local and realistic mechanism is sufficient to make the problem solvable. In particular, we give a very efficient protocol that solves Terminating Line Transformation when all nodes are initially identical. The latter implies that the model computes with termination any symmetric predicate computable by a Turing Machine of space Θ(n2).
]]></description>
<dc:subject>artificial-chemistries artificial-life self-organization distributed-processing to-understand to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:c512bf71b9ff/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-chemistries"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:self-organization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:distributed-processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-understand"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1503.01913">
    <title>[1503.01913] Terminating Distributed Construction of Shapes and Patterns in a Fair Solution of Automata</title>
    <dc:date>2017-04-29T20:46:16+00:00</dc:date>
    <link>https://arxiv.org/abs/1503.01913</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We consider a solution of automata similar to Population Protocols and Network Constructors. The automata (or nodes) move passively in a well-mixed solution and can cooperate by interacting in pairs. Every such interaction may result in an update of the local states of the nodes. Additionally, the nodes may also choose to connect to each other in order to start forming some required structure. We may think of such nodes as the smallest possible programmable pieces of matter. The model that we introduce here is a more applied version of Network Constructors, imposing physical (or geometrical) constraints on the connections. Each node can connect to other nodes only via a very limited number of local ports, therefore at any given time it has only a bounded number of neighbors. Connections are always made at unit distance and are perpendicular to connections of neighboring ports. We show that this restricted model is still capable of forming very practical 2D or 3D shapes. We provide direct constructors for some basic shape construction problems. We then develop new techniques for determining the constructive capabilities of our model. One of the main novelties of our approach, concerns our attempt to overcome the inability of such systems to detect termination. In particular, we exploit the assumptions that the system is well-mixed and has a unique leader, in order to give terminating protocols that are correct with high probability (w.h.p.). This allows us to develop terminating subroutines that can be sequentially composed to form larger modular protocols. One of our main results is a terminating protocol counting the size n of the system w.h.p.. We then use this protocol as a subroutine in order to develop our universal constructors, establishing that the nodes can self-organize w.h.p. into arbitrarily complex shapes while still detecting termination of the construction.
]]></description>
<dc:subject>self-organization self-assembly distributed-processing rather-interesting artificial-life simulation to-write-about nudge-targets consider:looking-to-see</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:f13381f926c1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:self-organization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:self-assembly"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:distributed-processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:looking-to-see"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1704.07589">
    <title>[1704.07589] Model of knowledge transfer within an organisation</title>
    <dc:date>2017-04-26T11:44:21+00:00</dc:date>
    <link>https://arxiv.org/abs/1704.07589</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Many studies show that the acquisition of knowledge is the key to build competitive advantage of companies. We propose a simple model of knowledge transfer within the organization and we implement the proposed model using cellular automata technique. In this paper the organisation is considered in the context of complex systems. In this perspective, the main role in organisation is played by the network of informal contacts and the distributed leadership. The goal of this paper is to check which factors influence the efficiency and effectiveness of knowledge transfer. Our studies indicate a significant role of initial concentration of chunks of knowledge for knowledge transfer process, and the results suggest taking action in the organisation to shorten the distance (social distance) between people with different levels of knowledge, or working out incentives to share knowledge.
]]></description>
<dc:subject>organizational-behavior agent-based artificial-life rather-interesting simulation to-write-about consider:looking-to-see</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:571fa93e705b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:organizational-behavior"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:agent-based"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:looking-to-see"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1603.08269">
    <title>[1603.08269] Equivalence of Deterministic walks on regular lattices on the plane</title>
    <dc:date>2017-04-22T12:24:04+00:00</dc:date>
    <link>https://arxiv.org/abs/1603.08269</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We consider deterministic walks on square, triangular and hexagonal two dimensional lattices. In each case, there is a scatterer at every site that can be in one of two states that force the walker to turn either to his/her immediate right or left. After the walker is scattered, the scatterer changes state. A lattice with an arrangement of scatterers is an environment. We show that there are only two environments for which the scattering rules are injective, mirrors or rotators, on the three lattices. On hexagonal lattices, B. Z. Webb and E. G. D. Cohen, proved that given an initial position and velocity of the walker and an environment of one type of scatterers, mirrrors or rotators, there is an environment of the other type such that the walks on both environments are equivalent, meaning they visit the same sites at the same time steps. We prove the equivalence of walks on square and triangular lattices and include a proof of the equivalence of walks on hexagonal lattices. The proofs are based both on the geometry of the lattice and the structure of the scattering rule.]]></description>
<dc:subject>cellular-automata artificial-life discrete-mathematics rather-interesting to-write-about computational-complexity</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:312e3b2012a5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cellular-automata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:discrete-mathematics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:computational-complexity"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1012.1332">
    <title>[1012.1332] Time-Symmetric Cellular Automata</title>
    <dc:date>2017-04-22T12:19:05+00:00</dc:date>
    <link>https://arxiv.org/abs/1012.1332</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Together with the concept of reversibility, another relevant physical notion is time-symmetry, which expresses that there is no way of distinguishing between backward and forward time directions. This notion, found in physical theories, has been neglected in the area of discrete dynamical systems. Here we formalize it in the context of cellular automata and establish some basic facts and relations. We also state some open problems that may encourage further research on the topic.]]></description>
<dc:subject>cellular-automata artificial-life complexology computational-complexity information-theory representation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:a47488701a5f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cellular-automata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:complexology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:computational-complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:information-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:representation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1611.09149">
    <title>[1611.09149] Dynamic landscape models of coevolutionary games</title>
    <dc:date>2017-04-13T12:46:46+00:00</dc:date>
    <link>https://arxiv.org/abs/1611.09149</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Players of coevolutionary games may update not only their strategies but also their networks of interaction. Based on interpreting the payoff of players as fitness, dynamic landscape models are proposed. The modeling procedure is carried out for Prisoner's Dilemma (PD) and Snowdrift (SD) games that both use either birth--death (BD) or death--birth (DB) strategy updating. The main focus is on using dynamic fitness landscapes as a mathematical model of coevolutionary game dynamics. Hence, an alternative tool for analyzing coevolutionary games becomes available, and landscape measures such as modality, ruggedness and information content can be computed and analyzed. In addition, fixation properties of the games and quantifiers characterizing the interaction networks are calculated numerically. Relations are established between landscape properties expressed by landscape measures and quantifiers of coevolutionary game dynamics such as fixation probabilities, fixation times and network properties.
]]></description>
<dc:subject>evolutionary-economics agent-based rather-interesting to-write-about artificial-life game-theory</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:b356e81411b3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:evolutionary-economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:agent-based"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:game-theory"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1309.1837">
    <title>[1309.1837] Evolution and non-equilibrium physics. A study of the Tangled Nature Model</title>
    <dc:date>2017-03-24T12:45:27+00:00</dc:date>
    <link>https://arxiv.org/abs/1309.1837</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We argue that the stochastic dynamics of interacting agents which replicate, mutate and die constitutes a non-equilibrium physical process akin to aging in complex materials. Specifically, our study uses extensive computer simulations of the Tangled Nature Model (TNM) of biological evolution to show that punctuated equilibria successively generated by the model's dynamics have increasing entropy and are separated by increasing entropic barriers. We further show that these states are organized in a hierarchy and that limiting the values of possible interactions to a finite interval leads to stationary fluctuations within a component of the latter. A coarse-grained description based on the temporal statistics of quakes, the events leading from one component of the hierarchy to the next, accounts for the logarithmic growth of the population and the decaying rate of change of macroscopic variables. Finally, we question the role of fitness in large scale evolution models and speculate on the possible evolutionary role of rejuvenation and memory effects.
]]></description>
<dc:subject>theoretical-biology artificial-life complexology ecology Bak-Sneppen-stuff fitness-landscapes Oh Physics!</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:f5e82efaeef8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:theoretical-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:complexology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:ecology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:Bak-Sneppen-stuff"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:fitness-landscapes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:Oh"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:Physics!"/>
</rdf:Bag></taxo:topics>
</item>
</rdf:RDF>