Alexander Hölken , Sean Kugele , Albert Newen , Stan Franklin
{"title":"具象自我与叙事自我之间的互动建模:LIDA内部自我模式的动态","authors":"Alexander Hölken , Sean Kugele , Albert Newen , Stan Franklin","doi":"10.1016/j.cogsys.2023.03.002","DOIUrl":null,"url":null,"abstract":"<div><p><span>Despite lacking a generally accepted definition, Artificial General Intelligence (AGI) is commonly understood to refer to artificial agents possessing the capacity to build up a context-independent understanding of itself and the world and to generalize this knowledge across a multitude of contexts. In human agents, this capacity is, to a large degree, facilitated by processes of </span><em>self-directed learning</em>, during which agents voluntarily control the conditions under which episodes of learning and problem solving occur. Since self-directed learning depends on the degree of knowledge the agent has about various aspects of themselves (their bodily skills, their learning goal, etc.), an AGI implementation of this type of learning must build on a theory of how this self-knowledge is actualized and modified during the learning process. In this paper, we employ the <em>pattern theory of self</em> in order to characterize different aspects of an agent’s self that are relevant for self-directed learning. Such aspects include agent-internal cognitive states such as thoughts, emotions, and intentions, but also relational states such as action possibilities in the environment. Combinations of these aspects form a characteristic pattern, which is unique to each individual agent, with no one aspect being necessary or sufficient for the individuation of that agent’s self. Here, we focus on the interdependence of narrative and embodied aspects of the self-pattern, since they involve particularly salient challenges consisting in conceptualizing the interaction between propositional and motor representations.</p><p>In our paper, we model the reciprocal interaction of these aspects of the self-pattern within an individual cognitive agent. We do so by extending an approach by Ryan, Agrawal, & Franklin (2020), who laid the groundwork for the implementation of the pattern theory of self in the LIDA (Learning Intelligent Decision Agent) model. We describe how embodied and narrative aspects of an agent’s self-pattern are realized by patterns of interaction between different LIDA modules over time, and how interactions at multiple temporal scales allow the agent’s self-pattern to be both dynamically variable and relatively stable. Finally, we investigate the implications this view has for the creation of artificial agents that can benefit from self-directed learning, both in the context of deliberate planning and adaptive motor execution.</p></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Modeling interactions between the embodied and the narrative self: Dynamics of the self-pattern within LIDA\",\"authors\":\"Alexander Hölken , Sean Kugele , Albert Newen , Stan Franklin\",\"doi\":\"10.1016/j.cogsys.2023.03.002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p><span>Despite lacking a generally accepted definition, Artificial General Intelligence (AGI) is commonly understood to refer to artificial agents possessing the capacity to build up a context-independent understanding of itself and the world and to generalize this knowledge across a multitude of contexts. In human agents, this capacity is, to a large degree, facilitated by processes of </span><em>self-directed learning</em>, during which agents voluntarily control the conditions under which episodes of learning and problem solving occur. Since self-directed learning depends on the degree of knowledge the agent has about various aspects of themselves (their bodily skills, their learning goal, etc.), an AGI implementation of this type of learning must build on a theory of how this self-knowledge is actualized and modified during the learning process. In this paper, we employ the <em>pattern theory of self</em> in order to characterize different aspects of an agent’s self that are relevant for self-directed learning. Such aspects include agent-internal cognitive states such as thoughts, emotions, and intentions, but also relational states such as action possibilities in the environment. Combinations of these aspects form a characteristic pattern, which is unique to each individual agent, with no one aspect being necessary or sufficient for the individuation of that agent’s self. Here, we focus on the interdependence of narrative and embodied aspects of the self-pattern, since they involve particularly salient challenges consisting in conceptualizing the interaction between propositional and motor representations.</p><p>In our paper, we model the reciprocal interaction of these aspects of the self-pattern within an individual cognitive agent. We do so by extending an approach by Ryan, Agrawal, & Franklin (2020), who laid the groundwork for the implementation of the pattern theory of self in the LIDA (Learning Intelligent Decision Agent) model. We describe how embodied and narrative aspects of an agent’s self-pattern are realized by patterns of interaction between different LIDA modules over time, and how interactions at multiple temporal scales allow the agent’s self-pattern to be both dynamically variable and relatively stable. Finally, we investigate the implications this view has for the creation of artificial agents that can benefit from self-directed learning, both in the context of deliberate planning and adaptive motor execution.</p></div>\",\"PeriodicalId\":55242,\"journal\":{\"name\":\"Cognitive Systems Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2023-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cognitive Systems Research\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1389041723000335\",\"RegionNum\":3,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cognitive Systems Research","FirstCategoryId":"102","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1389041723000335","RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Modeling interactions between the embodied and the narrative self: Dynamics of the self-pattern within LIDA
Despite lacking a generally accepted definition, Artificial General Intelligence (AGI) is commonly understood to refer to artificial agents possessing the capacity to build up a context-independent understanding of itself and the world and to generalize this knowledge across a multitude of contexts. In human agents, this capacity is, to a large degree, facilitated by processes of self-directed learning, during which agents voluntarily control the conditions under which episodes of learning and problem solving occur. Since self-directed learning depends on the degree of knowledge the agent has about various aspects of themselves (their bodily skills, their learning goal, etc.), an AGI implementation of this type of learning must build on a theory of how this self-knowledge is actualized and modified during the learning process. In this paper, we employ the pattern theory of self in order to characterize different aspects of an agent’s self that are relevant for self-directed learning. Such aspects include agent-internal cognitive states such as thoughts, emotions, and intentions, but also relational states such as action possibilities in the environment. Combinations of these aspects form a characteristic pattern, which is unique to each individual agent, with no one aspect being necessary or sufficient for the individuation of that agent’s self. Here, we focus on the interdependence of narrative and embodied aspects of the self-pattern, since they involve particularly salient challenges consisting in conceptualizing the interaction between propositional and motor representations.
In our paper, we model the reciprocal interaction of these aspects of the self-pattern within an individual cognitive agent. We do so by extending an approach by Ryan, Agrawal, & Franklin (2020), who laid the groundwork for the implementation of the pattern theory of self in the LIDA (Learning Intelligent Decision Agent) model. We describe how embodied and narrative aspects of an agent’s self-pattern are realized by patterns of interaction between different LIDA modules over time, and how interactions at multiple temporal scales allow the agent’s self-pattern to be both dynamically variable and relatively stable. Finally, we investigate the implications this view has for the creation of artificial agents that can benefit from self-directed learning, both in the context of deliberate planning and adaptive motor execution.
期刊介绍:
Cognitive Systems Research is dedicated to the study of human-level cognition. As such, it welcomes papers which advance the understanding, design and applications of cognitive and intelligent systems, both natural and artificial.
The journal brings together a broad community studying cognition in its many facets in vivo and in silico, across the developmental spectrum, focusing on individual capacities or on entire architectures. It aims to foster debate and integrate ideas, concepts, constructs, theories, models and techniques from across different disciplines and different perspectives on human-level cognition. The scope of interest includes the study of cognitive capacities and architectures - both brain-inspired and non-brain-inspired - and the application of cognitive systems to real-world problems as far as it offers insights relevant for the understanding of cognition.
Cognitive Systems Research therefore welcomes mature and cutting-edge research approaching cognition from a systems-oriented perspective, both theoretical and empirically-informed, in the form of original manuscripts, short communications, opinion articles, systematic reviews, and topical survey articles from the fields of Cognitive Science (including Philosophy of Cognitive Science), Artificial Intelligence/Computer Science, Cognitive Robotics, Developmental Science, Psychology, and Neuroscience and Neuromorphic Engineering. Empirical studies will be considered if they are supplemented by theoretical analyses and contributions to theory development and/or computational modelling studies.