If human societies are so complex, then how can we hope to understand them? Artificial Life gives us one answer. The field of Artificial Life comprises a diverse set of introspective studies that largely ask the same questions, albeit from many different perspectives: Why are we here? Who are we? Why do we behave as we do? Starting with the origins of life provides us with fascinating answers to some of these questions. However, some researchers choose to bring their studies closer to the present day. We are, after all, human. It has been a few billion years since our ancestors were self-replicating molecules. Thus more direct studies of ourselves and our human societies can reveal truths that may lead to practical knowledge. The articles in this special issue bring together scientists who choose to perform this kind of research. Expanded from submissions to our annual Agent-Based Modelling of Human Behaviour Workshop, the studies share similar methods, all using variations of agent-based modeling (ABM) to ask their own what-if questions. As guest editors, we believe such collections help bring together and enhance such research by sharing ideas. While ABM research—out of necessity—is often highly specialized toward the hypotheses and phenomena under study, the research methodology is shared by all. We formulate our hypothesis, develop our agent-based model of the relevant aspects of reality, and run experiments to gather evidence that may support or refute the hypothesis. An experimental model that supports the hypothesis may not prove that reality follows this approach or agrees with this result, but it indicates that there exists a specific set of conditions that, if found to be true elsewhere, may produce the same result. Modeling tells us about trends, about possible likelihoods. Our ABMs show us what will result if our assumptions are valid and why, whether we are examining civil violence, app stores, the economy, fish markets, language evolution, or energy consumption. When we study human societies, ABMs are the tools of choice for obvious reasons: It is not ethical or safe to play what-if experiments with ourselves. The researchers in this special issue demonstrate the exciting potential in ABM. We can create our own safe virtual worlds and make discoveries that enlighten us about ourselves.
{"title":"The “Agent-Based Modeling for Human Behavior” Special Issue","authors":"Soo Ling Lim;Peter J. Bentley","doi":"10.1162/artl_e_00394","DOIUrl":"10.1162/artl_e_00394","url":null,"abstract":"If human societies are so complex, then how can we hope to understand them? Artificial Life gives us one answer. The field of Artificial Life comprises a diverse set of introspective studies that largely ask the same questions, albeit from many different perspectives: Why are we here? Who are we? Why do we behave as we do? Starting with the origins of life provides us with fascinating answers to some of these questions. However, some researchers choose to bring their studies closer to the present day. We are, after all, human. It has been a few billion years since our ancestors were self-replicating molecules. Thus more direct studies of ourselves and our human societies can reveal truths that may lead to practical knowledge. The articles in this special issue bring together scientists who choose to perform this kind of research. Expanded from submissions to our annual Agent-Based Modelling of Human Behaviour Workshop, the studies share similar methods, all using variations of agent-based modeling (ABM) to ask their own what-if questions. As guest editors, we believe such collections help bring together and enhance such research by sharing ideas. While ABM research—out of necessity—is often highly specialized toward the hypotheses and phenomena under study, the research methodology is shared by all. We formulate our hypothesis, develop our agent-based model of the relevant aspects of reality, and run experiments to gather evidence that may support or refute the hypothesis. An experimental model that supports the hypothesis may not prove that reality follows this approach or agrees with this result, but it indicates that there exists a specific set of conditions that, if found to be true elsewhere, may produce the same result. Modeling tells us about trends, about possible likelihoods. Our ABMs show us what will result if our assumptions are valid and why, whether we are examining civil violence, app stores, the economy, fish markets, language evolution, or energy consumption. When we study human societies, ABMs are the tools of choice for obvious reasons: It is not ethical or safe to play what-if experiments with ourselves. The researchers in this special issue demonstrate the exciting potential in ABM. We can create our own safe virtual worlds and make discoveries that enlighten us about ourselves.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"29 1","pages":"1-2"},"PeriodicalIF":2.6,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10792470","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}
What role do affective feelings (feelings/emotions/moods) play in adaptive behaviour? What are the implications of this for understanding and developing artificial general intelligence? Leading theoretical models of brain function are beginning to shed light on these questions. While artificial agents have excelled within narrowly circumscribed and specialised domains, domain-general intelligence has remained an elusive goal in artificial intelligence research. By contrast, humans and nonhuman animals are characterised by a capacity for flexible behaviour and general intelligence. In this article I argue that computational models of mental phenomena in predictive processing theories of the brain are starting to reveal the mechanisms underpinning domain-general intelligence in biological agents, and can inform the understanding and development of artificial general intelligence. I focus particularly on approaches to computational phenomenology in the active inference framework. Specifically, I argue that computational mechanisms of affective feelings in active inference—affective self-modelling—are revealing of how biological agents are able to achieve flexible behavioural repertoires and general intelligence. I argue that (i) affective self-modelling functions to “tune” organisms to the most tractable goals in the environmental context; and (ii) affective and agentic self-modelling is central to the capacity to perform mental actions in goal-directed imagination and creative cognition. I use this account as a basis to argue that general intelligence of the level and kind found in biological agents will likely require machines to be implemented with analogues of affective self-modelling.
{"title":"Machines That Feel and Think: The Role of Affective Feelings and Mental Action in (Artificial) General Intelligence","authors":"George Deane","doi":"10.1162/artl_a_00368","DOIUrl":"10.1162/artl_a_00368","url":null,"abstract":"What role do affective feelings (feelings/emotions/moods) play in adaptive behaviour? What are the implications of this for understanding and developing artificial general intelligence? Leading theoretical models of brain function are beginning to shed light on these questions. While artificial agents have excelled within narrowly circumscribed and specialised domains, domain-general intelligence has remained an elusive goal in artificial intelligence research. By contrast, humans and nonhuman animals are characterised by a capacity for flexible behaviour and general intelligence. In this article I argue that computational models of mental phenomena in predictive processing theories of the brain are starting to reveal the mechanisms underpinning domain-general intelligence in biological agents, and can inform the understanding and development of artificial general intelligence. I focus particularly on approaches to computational phenomenology in the active inference framework. Specifically, I argue that computational mechanisms of affective feelings in active inference—affective self-modelling—are revealing of how biological agents are able to achieve flexible behavioural repertoires and general intelligence. I argue that (i) affective self-modelling functions to “tune” organisms to the most tractable goals in the environmental context; and (ii) affective and agentic self-modelling is central to the capacity to perform mental actions in goal-directed imagination and creative cognition. I use this account as a basis to argue that general intelligence of the level and kind found in biological agents will likely require machines to be implemented with analogues of affective self-modelling.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"28 3","pages":"289-309"},"PeriodicalIF":2.6,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40555518","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}
Bacterial chemotaxis in unicellular Escherichia coli, the simplest biological creature, enables it to perform effective searching behaviour even with a single sensor, achieved via a sequence of “tumbling” and “swimming” behaviours guided by gradient information. Recent studies show that suitable random walk strategies may guide the behaviour in the absence of gradient information. This article presents a novel and minimalistic biologically inspired search strategy inspired by bacterial chemotaxis and embodied intelligence concept: a concept stating that intelligent behaviour is a result of the interaction among the “brain,” body morphology including the sensory sensitivity tuned by the morphology, and the environment. Specifically, we present bacterial chemotaxis inspired searching behaviour with and without gradient information based on biological fluctuation framework: a mathematical framework that explains how biological creatures utilize noises in their behaviour. Via extensive simulation of a single sensor mobile robot that searches for a moving target, we will demonstrate how the effectiveness of the search depends on the sensory sensitivity and the inherent random walk strategies produced by the brain of the robot, comprising Ballistic, Levy, Brownian, and Stationary search. The result demonstrates the importance of embodied intelligence even in a behaviour inspired by the simplest creature.
{"title":"An Embodied Intelligence-Based Biologically Inspired Strategy for Searching a Moving Target","authors":"Julian K. P. Tan;Chee Pin Tan;Surya G. Nurzaman","doi":"10.1162/artl_a_00375","DOIUrl":"10.1162/artl_a_00375","url":null,"abstract":"Bacterial chemotaxis in unicellular Escherichia coli, the simplest biological creature, enables it to perform effective searching behaviour even with a single sensor, achieved via a sequence of “tumbling” and “swimming” behaviours guided by gradient information. Recent studies show that suitable random walk strategies may guide the behaviour in the absence of gradient information. This article presents a novel and minimalistic biologically inspired search strategy inspired by bacterial chemotaxis and embodied intelligence concept: a concept stating that intelligent behaviour is a result of the interaction among the “brain,” body morphology including the sensory sensitivity tuned by the morphology, and the environment. Specifically, we present bacterial chemotaxis inspired searching behaviour with and without gradient information based on biological fluctuation framework: a mathematical framework that explains how biological creatures utilize noises in their behaviour. Via extensive simulation of a single sensor mobile robot that searches for a moving target, we will demonstrate how the effectiveness of the search depends on the sensory sensitivity and the inherent random walk strategies produced by the brain of the robot, comprising Ballistic, Levy, Brownian, and Stationary search. The result demonstrates the importance of embodied intelligence even in a behaviour inspired by the simplest creature.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"28 3","pages":"348-368"},"PeriodicalIF":2.6,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40636025","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}
Modularity is a desirable property for embodied agents, as it could foster their suitability to different domains by disassembling them into transferable modules that can be reassembled differently. We focus on a class of embodied agents known as voxel-based soft robots (VSRs). They are aggregations of elastic blocks of soft material; as such, their morphologies are intrinsically modular. Nevertheless, controllers used until now for VSRs act as abstract, disembodied processing units: Disassembling such controllers for the purpose of module transferability is a challenging problem. Thus, the full potential of modularity for VSRs still remains untapped. In this work, we propose a novel self-organizing, embodied neural controller for VSRs. We optimize it for a given task and morphology by means of evolutionary computation: While evolving, the controller spreads across the VSR morphology in a way that permits emergence of modularity. We experimentally investigate whether such a controller (i) is effective and (ii) allows tuning of its degree of modularity, and with what kind of impact. To this end, we consider the task of locomotion on rugged terrains and evolve controllers for two morphologies. Our experiments confirm that our self-organizing, embodied controller is indeed effective. Moreover, by mimicking the structural modularity observed in biological neural networks, different levels of modularity can be achieved. Our findings suggest that the self-organization of modularity could be the basis for an automatic pipeline for assembling, disassembling, and reassembling embodied agents.
{"title":"Evolving Modularity in Soft Robots Through an Embodied and Self-Organizing Neural Controller","authors":"Federico Pigozzi;Eric Medvet","doi":"10.1162/artl_a_00367","DOIUrl":"10.1162/artl_a_00367","url":null,"abstract":"Modularity is a desirable property for embodied agents, as it could foster their suitability to different domains by disassembling them into transferable modules that can be reassembled differently. We focus on a class of embodied agents known as voxel-based soft robots (VSRs). They are aggregations of elastic blocks of soft material; as such, their morphologies are intrinsically modular. Nevertheless, controllers used until now for VSRs act as abstract, disembodied processing units: Disassembling such controllers for the purpose of module transferability is a challenging problem. Thus, the full potential of modularity for VSRs still remains untapped. In this work, we propose a novel self-organizing, embodied neural controller for VSRs. We optimize it for a given task and morphology by means of evolutionary computation: While evolving, the controller spreads across the VSR morphology in a way that permits emergence of modularity. We experimentally investigate whether such a controller (i) is effective and (ii) allows tuning of its degree of modularity, and with what kind of impact. To this end, we consider the task of locomotion on rugged terrains and evolve controllers for two morphologies. Our experiments confirm that our self-organizing, embodied controller is indeed effective. Moreover, by mimicking the structural modularity observed in biological neural networks, different levels of modularity can be achieved. Our findings suggest that the self-organization of modularity could be the basis for an automatic pipeline for assembling, disassembling, and reassembling embodied agents.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"28 3","pages":"322-347"},"PeriodicalIF":2.6,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40592810","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}
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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：