Stefan Dvoretskii;Ziyi Gong;Ankit Gupta;Jesse Parent;Bradly Alicea
Connecting brain and behavior is a longstanding issue in the areas of behavioral science, artificial intelligence, and neurobiology. As is standard among models of artificial and biological neural networks, an analogue of the fully mature brain is presented as a blank slate. However, this does not consider the realities of biological development and developmental learning. Our purpose is to model the development of an artificial organism that exhibits complex behaviors. We introduce three alternate approaches to demonstrate how developmental embodied agents can be implemented. The resulting developmental Braitenberg vehicles (dBVs) will generate behaviors ranging from stimulus responses to group behavior that resembles collective motion. We will situate this work in the domain of artificial brain networks along with broader themes such as embodied cognition, feedback, and emergence. Our perspective is exemplified by three software instantiations that demonstrate how a BV-genetic algorithm hybrid model, a multisensory Hebbian learning model, and multi-agent approaches can be used to approach BV development. We introduce use cases such as optimized spatial cognition (vehicle-genetic algorithm hybrid model), hinges connecting behavioral and neural models (multisensory Hebbian learning model), and cumulative classification (multi-agent approaches). In conclusion, we consider future applications of the developmental neurosimulation approach.
{"title":"Braitenberg Vehicles as Developmental Neurosimulation","authors":"Stefan Dvoretskii;Ziyi Gong;Ankit Gupta;Jesse Parent;Bradly Alicea","doi":"10.1162/artl_a_00384","DOIUrl":"10.1162/artl_a_00384","url":null,"abstract":"Connecting brain and behavior is a longstanding issue in the areas of behavioral science, artificial intelligence, and neurobiology. As is standard among models of artificial and biological neural networks, an analogue of the fully mature brain is presented as a blank slate. However, this does not consider the realities of biological development and developmental learning. Our purpose is to model the development of an artificial organism that exhibits complex behaviors. We introduce three alternate approaches to demonstrate how developmental embodied agents can be implemented. The resulting developmental Braitenberg vehicles (dBVs) will generate behaviors ranging from stimulus responses to group behavior that resembles collective motion. We will situate this work in the domain of artificial brain networks along with broader themes such as embodied cognition, feedback, and emergence. Our perspective is exemplified by three software instantiations that demonstrate how a BV-genetic algorithm hybrid model, a multisensory Hebbian learning model, and multi-agent approaches can be used to approach BV development. We introduce use cases such as optimized spatial cognition (vehicle-genetic algorithm hybrid model), hinges connecting behavioral and neural models (multisensory Hebbian learning model), and cumulative classification (multi-agent approaches). In conclusion, we consider future applications of the developmental neurosimulation approach.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"28 3","pages":"369-395"},"PeriodicalIF":2.6,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40636023","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}
We had the great pleasure of organising the first virtual workshop on Embodied Intelligence, held on March 24–26, 2021. After the long struggle of more than a year with the pandemic, all of us were in strong need of interdisciplinary cross-fertilization events, even in a severely limited virtual setting. Even though it was a difficult time to organise anything, we had the luck of attracting over 1,000 registered participants to this event, with more than 100 presentations along with many active debates and discussions. Some of these lectures and debates are available at https://embodied-intelligence.org/. Because of the very successful event, we decided to organise this Special Issue on Embodied Intelligence in the Artificial Life journal to capture some of the discussions and document them in the format of journal publications. For this reason, the authors and reviewers of this special issue were mostly participants of the workshop. We are excited to deliver this issue to reflect the progress and challenges in this research field. The articles included in this special issue are as follows. “Machines that Feel and Think: The Role of Affective Feelings and Mental Action in (Artificial) General Intelligence” by George Deane discusses the roles of feelings, emotions, and moods for understanding biological intelligence and achieving artificial general intelligence. With ongoing research on active inference and self-modelling, the article argues that research in “affective feelings” plays increasingly essential roles to obtain a better understanding of computational phenomenology. “The Enactive and Interactive Dimensions of AI: Ingenuity and Imagination Through the Lens of Art and Music” by Maki Sato and Jonathan McKinney discusses the contributions of embodied and enactive approaches to AI, with a detailed analysis of an aspect of Japanese philosophy in terms of interactivity and contingent dimensions. “Evolving Modularity in Soft Robots Through an Embodied and Self-Organizing Neural Controller” by Federico Pigozzi and Eric Medvet presents research achievements in evolved soft robots. The roles of morphologies and the distributed nature of control architecture were analyzed with respect to the evolution of modularity in various simulated agents. “Braitenberg Vehicles as Developmental Neurosimulation” by Stefan Dvoretskii et al. presents recent progress in research in the developmental approach applied to the neural network of Braitenberg vehicles. Implementation of the basic principles from developmental sciences was shown to lead to the emergence of simple cognitive processes such as feedback, spatial perception, and collective behaviours. “An Embodied Intelligence-Based Biologically Inspired Strategy for Searching a Moving Target” by Julian K. P. Tan et al. reported recent analysis on search behaviours of simulated agents inspired by E. coli. The effect of embodiment was investigated to explain how simple biological systems can take advantage of it
{"title":"Editorial Introduction to the Special Issue on Embodied Intelligence","authors":"Fumiya Iida;Josie Hughes","doi":"10.1162/artl_e_00386","DOIUrl":"10.1162/artl_e_00386","url":null,"abstract":"We had the great pleasure of organising the first virtual workshop on Embodied Intelligence, held on March 24–26, 2021. After the long struggle of more than a year with the pandemic, all of us were in strong need of interdisciplinary cross-fertilization events, even in a severely limited virtual setting. Even though it was a difficult time to organise anything, we had the luck of attracting over 1,000 registered participants to this event, with more than 100 presentations along with many active debates and discussions. Some of these lectures and debates are available at https://embodied-intelligence.org/. Because of the very successful event, we decided to organise this Special Issue on Embodied Intelligence in the Artificial Life journal to capture some of the discussions and document them in the format of journal publications. For this reason, the authors and reviewers of this special issue were mostly participants of the workshop. We are excited to deliver this issue to reflect the progress and challenges in this research field. The articles included in this special issue are as follows. “Machines that Feel and Think: The Role of Affective Feelings and Mental Action in (Artificial) General Intelligence” by George Deane discusses the roles of feelings, emotions, and moods for understanding biological intelligence and achieving artificial general intelligence. With ongoing research on active inference and self-modelling, the article argues that research in “affective feelings” plays increasingly essential roles to obtain a better understanding of computational phenomenology. “The Enactive and Interactive Dimensions of AI: Ingenuity and Imagination Through the Lens of Art and Music” by Maki Sato and Jonathan McKinney discusses the contributions of embodied and enactive approaches to AI, with a detailed analysis of an aspect of Japanese philosophy in terms of interactivity and contingent dimensions. “Evolving Modularity in Soft Robots Through an Embodied and Self-Organizing Neural Controller” by Federico Pigozzi and Eric Medvet presents research achievements in evolved soft robots. The roles of morphologies and the distributed nature of control architecture were analyzed with respect to the evolution of modularity in various simulated agents. “Braitenberg Vehicles as Developmental Neurosimulation” by Stefan Dvoretskii et al. presents recent progress in research in the developmental approach applied to the neural network of Braitenberg vehicles. Implementation of the basic principles from developmental sciences was shown to lead to the emergence of simple cognitive processes such as feedback, spatial perception, and collective behaviours. “An Embodied Intelligence-Based Biologically Inspired Strategy for Searching a Moving Target” by Julian K. P. Tan et al. reported recent analysis on search behaviours of simulated agents inspired by E. coli. The effect of embodiment was investigated to explain how simple biological systems can take advantage of it","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"28 3","pages":"287-288"},"PeriodicalIF":2.6,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40678733","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}
Dualisms are pervasive. The divisions between the rational mind, the physical body, and the external natural world have set the stage for the successes and failures of contemporary cognitive science and artificial intelligence.1 Advanced machine learning (ML) and artificial intelligence (AI) systems have been developed to draw art and compose music. Many take these facts as calls for a radical shift in our values and turn to questions about AI ethics, rights, and personhood. While the discussion of agency and rights is not wrong in principle, it is a form of misdirection in the current circumstances. Questions about an artificial agency can only come after a genuine reconciliation of human interactivity, creativity, and embodiment. This kind of challenge has both moral and theoretical force. In this article, the authors intend to contribute to embodied and enactive approaches to AI by exploring the interactive and contingent dimensions of machines through the lens of Japanese philosophy. One important takeaway from this project is that AI/ML systems should be recognized as powerful tools or instruments rather than as agents themselves.
{"title":"The Enactive and Interactive Dimensions of AI: Ingenuity and Imagination Through the Lens of Art and Music","authors":"Maki Sato;Jonathan McKinney","doi":"10.1162/artl_a_00376","DOIUrl":"10.1162/artl_a_00376","url":null,"abstract":"Dualisms are pervasive. The divisions between the rational mind, the physical body, and the external natural world have set the stage for the successes and failures of contemporary cognitive science and artificial intelligence.1 Advanced machine learning (ML) and artificial intelligence (AI) systems have been developed to draw art and compose music. Many take these facts as calls for a radical shift in our values and turn to questions about AI ethics, rights, and personhood. While the discussion of agency and rights is not wrong in principle, it is a form of misdirection in the current circumstances. Questions about an artificial agency can only come after a genuine reconciliation of human interactivity, creativity, and embodiment. This kind of challenge has both moral and theoretical force. In this article, the authors intend to contribute to embodied and enactive approaches to AI by exploring the interactive and contingent dimensions of machines through the lens of Japanese philosophy. One important takeaway from this project is that AI/ML systems should be recognized as powerful tools or instruments rather than as agents themselves.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"28 3","pages":"310-321"},"PeriodicalIF":2.6,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40636024","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}
We implement an agent-based simulation of the response threshold model of reproductive division of labor. Ants in our simulation must perform two tasks in their environment: forage and reproduce. The colony is capable of allocating ant resources to these roles using different division of labor strategies via genetic architectures and plasticity mechanisms. We find that the deterministic allocation strategy of the response threshold model is more robust than the probabilistic allocation strategy. The deterministic allocation strategy is also capable of evolving complex solutions to colony problems like niche construction and recovery from the loss of the breeding caste. In addition, plasticity mechanisms had both positive and negative influence on the emergence of reproductive division of labor. The combination of plasticity mechanisms has an additive and sometimes emergent impact.
{"title":"Deterministic Response Threshold Models of Reproductive Division of Labor Are More Robust Than Probabilistic Models in Artificial Ants","authors":"Chris Marriott;Peter Bae;Jobran Chebib","doi":"10.1162/artl_a_00369","DOIUrl":"10.1162/artl_a_00369","url":null,"abstract":"We implement an agent-based simulation of the response threshold model of reproductive division of labor. Ants in our simulation must perform two tasks in their environment: forage and reproduce. The colony is capable of allocating ant resources to these roles using different division of labor strategies via genetic architectures and plasticity mechanisms. We find that the deterministic allocation strategy of the response threshold model is more robust than the probabilistic allocation strategy. The deterministic allocation strategy is also capable of evolving complex solutions to colony problems like niche construction and recovery from the loss of the breeding caste. In addition, plasticity mechanisms had both positive and negative influence on the emergence of reproductive division of labor. The combination of plasticity mechanisms has an additive and sometimes emergent impact.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"28 2","pages":"264-286"},"PeriodicalIF":2.6,"publicationDate":"2022-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40140868","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}
Discrete gene regulatory networks (GRNs) play a vital role in the study of robustness and modularity. A common method of evaluating the robustness of GRNs is to measure their ability to regulate a set of perturbed gene activation patterns back to their unperturbed forms. Usually, perturbations are obtained by collecting random samples produced by a predefined distribution of gene activation patterns. This sampling method introduces stochasticity, in turn inducing dynamicity. This dynamicity is imposed on top of an already complex fitness landscape. So where sampling is used, it is important to understand which effects arise from the structure of the fitness landscape, and which arise from the dynamicity imposed on it. Stochasticity of the fitness function also causes difficulties in reproducibility and in post-experimental analyses. We develop a deterministic distributional fitness evaluation by considering the complete distribution of gene activity patterns, so as to avoid stochasticity in fitness assessment. This fitness evaluation facilitates repeatability. Its determinism permits us to ascertain theoretical bounds on the fitness, and thus to identify whether the algorithm has reached a global optimum. It enables us to differentiate the effects of the problem domain from those of the noisy fitness evaluation, and thus to resolve two remaining anomalies in the behaviour of the problem domain of Espinosa-Soto and A. Wagner (2010). We also reveal some properties of solution GRNs that lead them to be robust and modular, leading to a deeper understanding of the nature of the problem domain. We conclude by discussing potential directions toward simulating and understanding the emergence of modularity in larger, more complex domains, which is key both to generating more useful modular solutions, and to understanding the ubiquity of modularity in biological systems.
{"title":"Resolving Anomalies in the Behaviour of a Modularity-Inducing Problem Domain with Distributional Fitness Evaluation","authors":"Zhenyue Qin;Tom Gedeon;R. I. McKay","doi":"10.1162/artl_a_00353","DOIUrl":"10.1162/artl_a_00353","url":null,"abstract":"Discrete gene regulatory networks (GRNs) play a vital role in the study of robustness and modularity. A common method of evaluating the robustness of GRNs is to measure their ability to regulate a set of perturbed gene activation patterns back to their unperturbed forms. Usually, perturbations are obtained by collecting random samples produced by a predefined distribution of gene activation patterns. This sampling method introduces stochasticity, in turn inducing dynamicity. This dynamicity is imposed on top of an already complex fitness landscape. So where sampling is used, it is important to understand which effects arise from the structure of the fitness landscape, and which arise from the dynamicity imposed on it. Stochasticity of the fitness function also causes difficulties in reproducibility and in post-experimental analyses. We develop a deterministic distributional fitness evaluation by considering the complete distribution of gene activity patterns, so as to avoid stochasticity in fitness assessment. This fitness evaluation facilitates repeatability. Its determinism permits us to ascertain theoretical bounds on the fitness, and thus to identify whether the algorithm has reached a global optimum. It enables us to differentiate the effects of the problem domain from those of the noisy fitness evaluation, and thus to resolve two remaining anomalies in the behaviour of the problem domain of Espinosa-Soto and A. Wagner (2010). We also reveal some properties of solution GRNs that lead them to be robust and modular, leading to a deeper understanding of the nature of the problem domain. We conclude by discussing potential directions toward simulating and understanding the emergence of modularity in larger, more complex domains, which is key both to generating more useful modular solutions, and to understanding the ubiquity of modularity in biological systems.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"28 2","pages":"240-263"},"PeriodicalIF":2.6,"publicationDate":"2022-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39910642","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}
Thomas Gabor;Steffen Illium;Maximilian Zorn;Cristian Lenta;Andy Mattausch;Lenz Belzner;Claudia Linnhoff-Popien
A key element of biological structures is self-replication. Neural networks are the prime structure used for the emergent construction of complex behavior in computers. We analyze how various network types lend themselves to self-replication. Backpropagation turns out to be the natural way to navigate the space of network weights and allows non-trivial self-replicators to arise naturally. We perform an in-depth analysis to show the self-replicators’ robustness to noise. We then introduce artificial chemistry environments consisting of several neural networks and examine their emergent behavior. In extension to this work’s previous version (Gabor et al., 2019), we provide an extensive analysis of the occurrence of fixpoint weight configurations within the weight space and an approximation of their respective attractor basins.
{"title":"Self-Replication in Neural Networks","authors":"Thomas Gabor;Steffen Illium;Maximilian Zorn;Cristian Lenta;Andy Mattausch;Lenz Belzner;Claudia Linnhoff-Popien","doi":"10.1162/artl_a_00359","DOIUrl":"10.1162/artl_a_00359","url":null,"abstract":"A key element of biological structures is self-replication. Neural networks are the prime structure used for the emergent construction of complex behavior in computers. We analyze how various network types lend themselves to self-replication. Backpropagation turns out to be the natural way to navigate the space of network weights and allows non-trivial self-replicators to arise naturally. We perform an in-depth analysis to show the self-replicators’ robustness to noise. We then introduce artificial chemistry environments consisting of several neural networks and examine their emergent behavior. In extension to this work’s previous version (Gabor et al., 2019), we provide an extensive analysis of the occurrence of fixpoint weight configurations within the weight space and an approximation of their respective attractor basins.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"28 2","pages":"205-223"},"PeriodicalIF":2.6,"publicationDate":"2022-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40139127","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}
We evolve floating point Sextic polynomial populations of genetic programming binary trees for up to a million generations. We observe continued innovation but this is limited by tree depth. We suggest that deep expressions are resilient to learning as they disperse information, impeding evolvability, and the adaptation of highly nested organisms, and we argue instead for open complexity. Programs with more than 2,000,000,000 instructions (depth 20,000) are created by crossover. To support unbounded long-term evolution experiments in genetic programming (GP), we use incremental fitness evaluation and both SIMD parallel AVX 512-bit instructions and 16 threads to yield performance equivalent to 1.1 trillion GP operations per second, 1.1 tera GPops, on an Intel Xeon Gold 6136 CPU 3.00GHz server.
我们进化浮点六分多项式种群遗传规划二叉树为多达一百万代。我们观察到持续的创新，但这受到树的深度的限制。我们认为深层表达对学习是有弹性的，因为它们分散了信息，阻碍了高度嵌套生物体的可进化性和适应性，我们主张开放的复杂性。具有超过20亿条指令(深度20,000)的程序是通过交叉创建的。为了支持遗传编程(GP)的无限长期进化实验，我们使用增量适应度评估和SIMD并行AVX 512位指令和16个线程，在Intel Xeon Gold 6136 CPU 3.00GHz服务器上产生相当于每秒1.1万亿GP操作，1.1兆gpop的性能。
{"title":"Long-Term Evolution Experiment with Genetic Programming","authors":"William B. Langdon;Wolfgang Banzhaf","doi":"10.1162/artl_a_00360","DOIUrl":"10.1162/artl_a_00360","url":null,"abstract":"We evolve floating point Sextic polynomial populations of genetic programming binary trees for up to a million generations. We observe continued innovation but this is limited by tree depth. We suggest that deep expressions are resilient to learning as they disperse information, impeding evolvability, and the adaptation of highly nested organisms, and we argue instead for open complexity. Programs with more than 2,000,000,000 instructions (depth 20,000) are created by crossover. To support unbounded long-term evolution experiments in genetic programming (GP), we use incremental fitness evaluation and both SIMD parallel AVX 512-bit instructions and 16 threads to yield performance equivalent to 1.1 trillion GP operations per second, 1.1 tera GPops, on an Intel Xeon Gold 6136 CPU 3.00GHz server.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"28 2","pages":"173-204"},"PeriodicalIF":2.6,"publicationDate":"2022-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40140869","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 environment is one of the key factors in the emergence of intelligent creatures, but it has received little attention within the Evolutionary Robotics literature. This article investigates the effects of changing environments on morphological and behavioral traits of evolvable robots. In particular, we extend a previous study by evolving robot populations under diverse changing-environment setups, varying the magnitude, frequency, duration, and dynamics of the changes. The results show that long-lasting effects of early generations occur not only when transitioning from easy to hard conditions, but also when going from hard to easy conditions. Furthermore, we demonstrate how the impact of environmental scaffolding is dependent on the nature of the environmental changes involved.
{"title":"How the History of Changing Environments Affects Traits of Evolvable Robot Populations","authors":"Karine Miras;A. E. Eiben","doi":"10.1162/artl_a_00379","DOIUrl":"10.1162/artl_a_00379","url":null,"abstract":"The environment is one of the key factors in the emergence of intelligent creatures, but it has received little attention within the Evolutionary Robotics literature. This article investigates the effects of changing environments on morphological and behavioral traits of evolvable robots. In particular, we extend a previous study by evolving robot populations under diverse changing-environment setups, varying the magnitude, frequency, duration, and dynamics of the changes. The results show that long-lasting effects of early generations occur not only when transitioning from easy to hard conditions, but also when going from hard to easy conditions. Furthermore, we demonstrate how the impact of environmental scaffolding is dependent on the nature of the environmental changes involved.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"28 2","pages":"224-239"},"PeriodicalIF":2.6,"publicationDate":"2022-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40404713","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 special issue highlights key selections from the 2019 Conference on Artificial Life, ALIFE’19, hosted by Newcastle University in Newcastle upon Tyne, UK. The annual conference addresses the synthesis and simulation of living systems. The
{"title":"Editorial: The 2019 Conference on Artificial Life Special Issue","authors":"Harold Fellermann;Rudolf M. Füchslin","doi":"10.1162/artl_e_00380","DOIUrl":"10.1162/artl_e_00380","url":null,"abstract":"This special issue highlights key selections from the 2019 Conference on Artificial Life, ALIFE’19, hosted by Newcastle University in Newcastle upon Tyne, UK. The annual conference addresses the synthesis and simulation of living systems. The","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"28 2","pages":"171-172"},"PeriodicalIF":2.6,"publicationDate":"2022-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49195582","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}
Paolo Euron’s “Uncanny Beauty: Aesthetics of Companionship, Love, and Sex Robots” lays out a vision for appreciating sex robots in aesthetic terms, centering the concept of “beauty” as a measure of what they can inspire culturally and existentially. In these comments I turn toward the field of human-robot interaction and the ethical challenges that inhabit the core of such an aesthetic turn.
{"title":"Comment on Paolo Euron’s “Uncanny Beauty: Aesthetics of Companionship, Love, and Sex Robots”","authors":"Thomas Arnold","doi":"10.1162/artl_a_00362","DOIUrl":"10.1162/artl_a_00362","url":null,"abstract":"Paolo Euron’s “Uncanny Beauty: Aesthetics of Companionship, Love, and Sex Robots” lays out a vision for appreciating sex robots in aesthetic terms, centering the concept of “beauty” as a measure of what they can inspire culturally and existentially. In these comments I turn toward the field of human-robot interaction and the ethical challenges that inhabit the core of such an aesthetic turn.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"28 1","pages":"124-127"},"PeriodicalIF":2.6,"publicationDate":"2022-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49220295","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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