{"title":"To Engineer an Angel, First Validate the Devil: Analyzing the “Could Be” in Artificial Life’s “Life as-It-Could-Be”","authors":"Alan Dorin;Susan Stepney","doi":"10.1162/ARTL.e.452","DOIUrl":"10.1162/ARTL.e.452","url":null,"abstract":"","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"31 4","pages":"397-400"},"PeriodicalIF":1.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145776723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The self-optimization (SO) model can be considered as the third operational mode of the classical Hopfield network, leveraging the power of associative memory to enhance optimization performance. Moreover, it has been argued to express characteristics of minimal agency, which renders it useful for the study of Artificial Life. In this article, we draw attention to another facet of the SO model: its capacity for creativity. Drawing on creativity studies, we argue that the model satisfies the necessary and sufficient conditions of a creative process. Moreover, we show that learning is needed to find creative outcomes above chance probability. Furthermore, we demonstrate that modifying the learning parameters in the SO model gives rise to four different regimes that can account for both creative products and inconclusive outcomes, thus providing a framework for studying and understanding the emergence of creative behaviors in artificial systems that learn.
{"title":"Untapped Potential in Self-Optimization of Hopfield Networks: The Creativity of Unsupervised Learning","authors":"Natalya Weber;Christian Guckelsberger;Tom Froese","doi":"10.1162/ARTL.a.10","DOIUrl":"10.1162/ARTL.a.10","url":null,"abstract":"The self-optimization (SO) model can be considered as the third operational mode of the classical Hopfield network, leveraging the power of associative memory to enhance optimization performance. Moreover, it has been argued to express characteristics of minimal agency, which renders it useful for the study of Artificial Life. In this article, we draw attention to another facet of the SO model: its capacity for creativity. Drawing on creativity studies, we argue that the model satisfies the necessary and sufficient conditions of a creative process. Moreover, we show that learning is needed to find creative outcomes above chance probability. Furthermore, we demonstrate that modifying the learning parameters in the SO model gives rise to four different regimes that can account for both creative products and inconclusive outcomes, thus providing a framework for studying and understanding the emergence of creative behaviors in artificial systems that learn.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"31 4","pages":"435-464"},"PeriodicalIF":1.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145338317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article introduces the System 0/1/2/3 framework as an extension of dual-process theory, employing a quad-process model of cognition. Expanding upon System 1 (fast, intuitive thinking) and System 2 (slow, deliberative thinking), we incorporate System 0, which represents precognitive embodied processes, and System 3, which encompasses collective intelligence and symbol emergence. We contextualize this model within Bergson’s philosophy by adopting multiscale time theory to unify the diverse temporal dynamics of cognition. System 0 emphasizes morphological computation and passive dynamics, illustrating how physical embodiment enables adaptive behavior without explicit neural processing. Systems 1 and 2 are explained from a constructive perspective, incorporating neurodynamical and artificial intelligence (AI) viewpoints. In System 3, we introduce collective predictive coding to explain how societal-level adaptation and symbol emergence operate over extended timescales. This comprehensive framework ranges from rapid embodied reactions to slow-evolving collective intelligence, offering a unified perspective on cognition across multiple timescales, levels of abstraction, and forms of human intelligence. The System 0/1/2/3 model provides a novel theoretical foundation for understanding the interplay between adaptive and cognitive processes, thereby opening new avenues for research in cognitive science, AI, robotics, and collective intelligence.
{"title":"System 0/1/2/3: Quad-Process Theory for Multitimescale Embodied Collective Cognitive Systems","authors":"Tadahiro Taniguchi;Yasushi Hirai;Masahiro Suzuki;Shingo Murata;Takato Horii;Kazutoshi Tanaka","doi":"10.1162/ARTL.a.12","DOIUrl":"10.1162/ARTL.a.12","url":null,"abstract":"This article introduces the System 0/1/2/3 framework as an extension of dual-process theory, employing a quad-process model of cognition. Expanding upon System 1 (fast, intuitive thinking) and System 2 (slow, deliberative thinking), we incorporate System 0, which represents precognitive embodied processes, and System 3, which encompasses collective intelligence and symbol emergence. We contextualize this model within Bergson’s philosophy by adopting multiscale time theory to unify the diverse temporal dynamics of cognition. System 0 emphasizes morphological computation and passive dynamics, illustrating how physical embodiment enables adaptive behavior without explicit neural processing. Systems 1 and 2 are explained from a constructive perspective, incorporating neurodynamical and artificial intelligence (AI) viewpoints. In System 3, we introduce collective predictive coding to explain how societal-level adaptation and symbol emergence operate over extended timescales. This comprehensive framework ranges from rapid embodied reactions to slow-evolving collective intelligence, offering a unified perspective on cognition across multiple timescales, levels of abstraction, and forms of human intelligence. The System 0/1/2/3 model provides a novel theoretical foundation for understanding the interplay between adaptive and cognitive processes, thereby opening new avenues for research in cognitive science, AI, robotics, and collective intelligence.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"31 4","pages":"465-496"},"PeriodicalIF":1.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145776714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The two-dimensional Turing machine is a promising but under used simulation tool for Artificial Life. Single-state 2-D Turing machines exhibit a variety of interesting behaviors, some of which have already been explored. Multistate 2-D Turing machines, despite their potential for simulating even more diverse behaviors, have received little attention to date. We demonstrate the potential of such automata for studying biological phenomena by showing how they can be used to simulate self-similar growth, the spread of disease, and self-reproduction. Some of the results presented here are from investigations that were performed around the time of Dewdney (1989), but they have not been published until now.
{"title":"Vants and Turmites","authors":"Greg Turk","doi":"10.1162/ARTL.a.9","DOIUrl":"10.1162/ARTL.a.9","url":null,"abstract":"The two-dimensional Turing machine is a promising but under used simulation tool for Artificial Life. Single-state 2-D Turing machines exhibit a variety of interesting behaviors, some of which have already been explored. Multistate 2-D Turing machines, despite their potential for simulating even more diverse behaviors, have received little attention to date. We demonstrate the potential of such automata for studying biological phenomena by showing how they can be used to simulate self-similar growth, the spread of disease, and self-reproduction. Some of the results presented here are from investigations that were performed around the time of Dewdney (1989), but they have not been published until now.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"31 4","pages":"401-434"},"PeriodicalIF":1.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145338283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This letter presents the idea that neural backpropagation is exploiting dendritic processing to enable individual neurons to perform autoencoding. Using a very simple connection weight search heuristic and artificial neural network model, the effects of interleaving autoencoding for each neuron in a hidden layer of a feedforward network are explored. This is contrasted with the equivalent standard layered approach to autoencoding. It is shown that such individualized processing is not detrimental and can improve network learning.
{"title":"Neurons as Autoencoders","authors":"Larry Bull","doi":"10.1162/artl_c_00461","DOIUrl":"10.1162/artl_c_00461","url":null,"abstract":"This letter presents the idea that neural backpropagation is exploiting dendritic processing to enable individual neurons to perform autoencoding. Using a very simple connection weight search heuristic and artificial neural network model, the effects of interleaving autoencoding for each neuron in a hidden layer of a feedforward network are explored. This is contrasted with the equivalent standard layered approach to autoencoding. It is shown that such individualized processing is not detrimental and can improve network learning.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"31 3","pages":"250-255"},"PeriodicalIF":1.5,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142633468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
An analysis of the language we use in scientific practice is critical to developing more rigorous and sound methodologies. This article argues that how certain methods of description are commonly employed in cognitive science risks obscuring important features of an agent’s cognition. We propose to make explicit a method of description whereby the concept of cognitive distinctions is the core principle. A model of referential communication is developed and analyzed as a platform to compare methods of description. We demonstrate that cognitive distinctions, realized in a graph theoretic formalism, better describe the behavior and perspective of a simple model agent than other, less systematic or natural language–dependent methods. We then consider how different descriptions relate to one another in the broader methodological framework of minimally cognitive behavior. Finally, we explore the consequences of, and challenges for, cognitive distinctions as a useful concept and method in the tool kit of cognitive scientists.
{"title":"Cognitive Distinctions as a Language for Cognitive Science: Comparing Methods of Description in a Model of Referential Communication","authors":"Thomas M. Gaul;Eduardo J. Izquierdo","doi":"10.1162/artl_a_00475","DOIUrl":"10.1162/artl_a_00475","url":null,"abstract":"An analysis of the language we use in scientific practice is critical to developing more rigorous and sound methodologies. This article argues that how certain methods of description are commonly employed in cognitive science risks obscuring important features of an agent’s cognition. We propose to make explicit a method of description whereby the concept of cognitive distinctions is the core principle. A model of referential communication is developed and analyzed as a platform to compare methods of description. We demonstrate that cognitive distinctions, realized in a graph theoretic formalism, better describe the behavior and perspective of a simple model agent than other, less systematic or natural language–dependent methods. We then consider how different descriptions relate to one another in the broader methodological framework of minimally cognitive behavior. Finally, we explore the consequences of, and challenges for, cognitive distinctions as a useful concept and method in the tool kit of cognitive scientists.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"31 3","pages":"345-367"},"PeriodicalIF":1.5,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144683586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Epigenetic tracking (ET) is a model of development that is capable of generating diverse, arbitrary, complex three-dimensional cellular structures starting from a single cell. The generated structures have a level of complexity (in terms of the number of cells) comparable to multicellular biological organisms. In this article, we investigate the evolvability of the development of a complex structure inspired by the “French flag” problem: an “Italian Anubis” (a three-dimensional, doglike figure patterned in three colors). Genes during development are triggered in ET at specific developmental stages, and the fitness of individuals during simulated evolution is calculated after a certain stage. When this evaluation stage was allowed to evolve, genes that were triggered at later stages of development tended to be incorporated into the genome later during evolutionary runs. This suggests the emergence of the property of terminal addition in this system. When the principle of terminal addition was explicitly incorporated into ET, and was the sole mechanism for introducing morphological innovation, evolvability improved markedly, leading to the development of structures much more closely approximating the target at a much lower computational cost.
表观遗传追踪（ET）是一种发育模型，能够从单细胞开始生成多样、任意、复杂的三维细胞结构。生成结构的复杂程度（就细胞数量而言）可与多细胞生物体相媲美。在这篇文章中，我们研究了受 "法国国旗 "问题启发的一种复杂结构："意大利阿努比斯"（一种三维的、以三种颜色为图案的狗状图形）的可演化性。发育过程中的基因在特定的发育阶段会在 ET 中被触发，模拟进化过程中个体的适应性会在某个阶段后被计算出来。当这一评估阶段被允许进化时，在发育后期被触发的基因往往会在进化运行的后期被纳入基因组。这表明在该系统中出现了末端加法的特性。当末端添加原则被明确纳入 ET，并成为引入形态创新的唯一机制时，进化性显著提高，从而以更低的计算成本开发出更接近目标的结构。
{"title":"Evolvability in Artificial Development of Large, Complex Structures and the Principle of Terminal Addition","authors":"Alessandro Fontana;Borys Wróbel","doi":"10.1162/artl_a_00460","DOIUrl":"10.1162/artl_a_00460","url":null,"abstract":"Epigenetic tracking (ET) is a model of development that is capable of generating diverse, arbitrary, complex three-dimensional cellular structures starting from a single cell. The generated structures have a level of complexity (in terms of the number of cells) comparable to multicellular biological organisms. In this article, we investigate the evolvability of the development of a complex structure inspired by the “French flag” problem: an “Italian Anubis” (a three-dimensional, doglike figure patterned in three colors). Genes during development are triggered in ET at specific developmental stages, and the fitness of individuals during simulated evolution is calculated after a certain stage. When this evaluation stage was allowed to evolve, genes that were triggered at later stages of development tended to be incorporated into the genome later during evolutionary runs. This suggests the emergence of the property of terminal addition in this system. When the principle of terminal addition was explicitly incorporated into ET, and was the sole mechanism for introducing morphological innovation, evolvability improved markedly, leading to the development of structures much more closely approximating the target at a much lower computational cost.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"31 3","pages":"276-288"},"PeriodicalIF":1.5,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142559566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The NK fitness landscape is a well-known model with which to study evolutionary dynamics in landscapes of different ruggedness. However, the model is static, and genomes are typically small, allowing observations over only a short adaptive period. Here we introduce an extension to the model that allows the experimenter to set the velocity at which the landscape changes independently from other parameters, such as the ruggedness or the mutation rate. We find that, similar to the previously observed complexity catastrophe, where evolution comes to a halt when environments become too complex due to overly high degrees of epistasis, here the same phenomenon occurs when changes happen too rapidly. Our expanded model also preserves essential properties of the static NK landscape, allowing for proper comparisons between static and dynamic landscapes.
{"title":"Continuous Evolution in the NK Treadmill Model","authors":"Priyanka Mehra;Arend Hintze","doi":"10.1162/artl_a_00467","DOIUrl":"10.1162/artl_a_00467","url":null,"abstract":"The NK fitness landscape is a well-known model with which to study evolutionary dynamics in landscapes of different ruggedness. However, the model is static, and genomes are typically small, allowing observations over only a short adaptive period. Here we introduce an extension to the model that allows the experimenter to set the velocity at which the landscape changes independently from other parameters, such as the ruggedness or the mutation rate. We find that, similar to the previously observed complexity catastrophe, where evolution comes to a halt when environments become too complex due to overly high degrees of epistasis, here the same phenomenon occurs when changes happen too rapidly. Our expanded model also preserves essential properties of the static NK landscape, allowing for proper comparisons between static and dynamic landscapes.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"31 3","pages":"256-275"},"PeriodicalIF":1.5,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143449978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Akarsh Kumar, Chris Lu, Louis Kirsch, Yujin Tang, Kenneth O Stanley, Phillip Isola, David Ha
With the recent Nobel Prize awarded for radical advances in protein discovery, foundation models (FMs) for exploring large combinatorial spaces promise to revolutionize many scientific fields. Artificial Life (ALife) has not yet integrated FMs, thus presenting a major opportunity for the field to alleviate the historical burden of relying chiefly on manual design and trial and error to discover the configurations of lifelike simulations. This article presents, for the first time, a successful realization of this opportunity using vision-language FMs. The proposed approach, called automated search for Artificial Life (ASAL), (a) finds simulations that produce target phenomena, (b) discovers simulations that generate temporally open-ended novelty, and (c) illuminates an entire space of interestingly diverse simulations. Because of the generality of FMs, ASAL works effectively across a diverse range of ALife substrates, including Boids, Particle Life, the Game of Life, Lenia, and neural cellular automata. A major result highlighting the potential of this technique is the discovery of previously unseen Lenia and Boids life-forms, as well as cellular automata that are open-ended like Conway's Game of Life. Additionally, the use of FMs allows for the quantification of previously qualitative phenomena in a human-aligned way. This new paradigm promises to accelerate ALife research beyond what is possible through human ingenuity alone.
随着最近的诺贝尔奖授予在蛋白质发现方面的激进进展，用于探索大组合空间的基础模型（FMs）有望彻底改变许多科学领域。人工生命（ALife）尚未集成FMs，因此为该领域提供了一个重要的机会，以减轻主要依靠人工设计和试错来发现类生命模拟配置的历史负担。本文首次使用视觉语言fm成功地实现了这一机会。提出的方法被称为人工生命的自动搜索（ASAL）， (a)发现产生目标现象的模拟，(b)发现产生暂时开放的新新性的模拟，(c)照亮了有趣的各种模拟的整个空间。由于FMs的通用性，ASAL在各种各样的ALife基质上有效地工作，包括Boids， Particle Life, Game of Life， Lenia和神经细胞自动机。突出这项技术潜力的一个主要结果是发现了以前看不见的Lenia和Boids生命形式，以及像Conway的生命游戏那样开放式的元胞自动机。此外，FMs的使用允许以与人类一致的方式对先前的定性现象进行量化。这种新模式有望加速生命研究，超越仅凭人类智慧所能达到的水平。
{"title":"Automating the Search for Artificial Life With Foundation Models.","authors":"Akarsh Kumar, Chris Lu, Louis Kirsch, Yujin Tang, Kenneth O Stanley, Phillip Isola, David Ha","doi":"10.1162/ARTL.a.8","DOIUrl":"https://doi.org/10.1162/ARTL.a.8","url":null,"abstract":"<p><p>With the recent Nobel Prize awarded for radical advances in protein discovery, foundation models (FMs) for exploring large combinatorial spaces promise to revolutionize many scientific fields. Artificial Life (ALife) has not yet integrated FMs, thus presenting a major opportunity for the field to alleviate the historical burden of relying chiefly on manual design and trial and error to discover the configurations of lifelike simulations. This article presents, for the first time, a successful realization of this opportunity using vision-language FMs. The proposed approach, called automated search for Artificial Life (ASAL), (a) finds simulations that produce target phenomena, (b) discovers simulations that generate temporally open-ended novelty, and (c) illuminates an entire space of interestingly diverse simulations. Because of the generality of FMs, ASAL works effectively across a diverse range of ALife substrates, including Boids, Particle Life, the Game of Life, Lenia, and neural cellular automata. A major result highlighting the potential of this technique is the discovery of previously unseen Lenia and Boids life-forms, as well as cellular automata that are open-ended like Conway's Game of Life. Additionally, the use of FMs allows for the quantification of previously qualitative phenomena in a human-aligned way. This new paradigm promises to accelerate ALife research beyond what is possible through human ingenuity alone.</p>","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"31 3","pages":"368-396"},"PeriodicalIF":1.5,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145002024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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