Abstract Legg and Hutter, as well as subsequent authors, considered intelligent agents through the lens of interaction with reward-giving environments, attempting to assign numeric intelligence measures to such agents, with the guiding principle that a more intelligent agent should gain higher rewards from environments in some aggregate sense. In this paper, we consider a related question: rather than measure numeric intelligence of one Legg-Hutter agent, how can we compare the relative intelligence of two Legg-Hutter agents? We propose an elegant answer based on the following insight: we can view Legg-Hutter agents as candidates in an election, whose voters are environments, letting each environment vote (via its rewards) which agent (if either) is more intelligent. This leads to an abstract family of comparators simple enough that we can prove some structural theorems about them. It is an open question whether these structural theorems apply to more practical intelligence measures.
{"title":"Intelligence via ultrafilters: structural properties of some intelligence comparators of deterministic Legg-Hutter agents","authors":"S. Alexander","doi":"10.2478/jagi-2019-0003","DOIUrl":"https://doi.org/10.2478/jagi-2019-0003","url":null,"abstract":"Abstract Legg and Hutter, as well as subsequent authors, considered intelligent agents through the lens of interaction with reward-giving environments, attempting to assign numeric intelligence measures to such agents, with the guiding principle that a more intelligent agent should gain higher rewards from environments in some aggregate sense. In this paper, we consider a related question: rather than measure numeric intelligence of one Legg-Hutter agent, how can we compare the relative intelligence of two Legg-Hutter agents? We propose an elegant answer based on the following insight: we can view Legg-Hutter agents as candidates in an election, whose voters are environments, letting each environment vote (via its rewards) which agent (if either) is more intelligent. This leads to an abstract family of comparators simple enough that we can prove some structural theorems about them. It is an open question whether these structural theorems apply to more practical intelligence measures.","PeriodicalId":247142,"journal":{"name":"Journal of Artificial General Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131247059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract This article systematically analyzes the problem of defining “artificial intelligence.” It starts by pointing out that a definition influences the path of the research, then establishes four criteria of a good working definition of a notion: being similar to its common usage, drawing a sharp boundary, leading to fruitful research, and as simple as possible. According to these criteria, the representative definitions in the field are analyzed. A new definition is proposed, according to it intelligence means “adaptation with insufficient knowledge and resources.” The implications of this definition are discussed, and it is compared with the other definitions. It is claimed that this definition sheds light on the solution of many existing problems and sets a sound foundation for the field.
{"title":"On Defining Artificial Intelligence","authors":"Pei Wang","doi":"10.2478/jagi-2019-0002","DOIUrl":"https://doi.org/10.2478/jagi-2019-0002","url":null,"abstract":"Abstract This article systematically analyzes the problem of defining “artificial intelligence.” It starts by pointing out that a definition influences the path of the research, then establishes four criteria of a good working definition of a notion: being similar to its common usage, drawing a sharp boundary, leading to fruitful research, and as simple as possible. According to these criteria, the representative definitions in the field are analyzed. A new definition is proposed, according to it intelligence means “adaptation with insufficient knowledge and resources.” The implications of this definition are discussed, and it is compared with the other definitions. It is claimed that this definition sheds light on the solution of many existing problems and sets a sound foundation for the field.","PeriodicalId":247142,"journal":{"name":"Journal of Artificial General Intelligence","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127268594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Claes Strannegård, Nils Svangård, David Lindström, Joscha Bach, Bas R. Steunebrink
Abstract A computational model for artificial animals (animats) interacting with real or artificial ecosystems is presented. All animats use the same mechanisms for learning and decisionmaking. Each animat has its own set of needs and its own memory structure that undergoes continuous development and constitutes the basis for decision-making. The decision-making mechanism aims at keeping the needs of the animat as satisfied as possible for as long as possible. Reward and punishment are defined in terms of changes to the level of need satisfaction. The learning mechanisms are driven by prediction error relating to reward and punishment and are of two kinds: multi-objective local Q-learning and structural learning that alter the architecture of the memory structures by adding and removing nodes. The animat model has the following key properties: (1) autonomy: it operates in a fully automatic fashion, without any need for interaction with human engineers. In particular, it does not depend on human engineers to provide goals, tasks, or seed knowledge. Still, it can operate either with or without human interaction; (2) generality: it uses the same learning and decision-making mechanisms in all environments, e.g. desert environments and forest environments and for all animats, e.g. frog animats and bee animats; and (3) adequacy: it is able to learn basic forms of animal skills such as eating, drinking, locomotion, and navigation. Eight experiments are presented. The results obtained indicate that (i) dynamic memory structures are strictly more powerful than static; (ii) it is possible to use a fixed generic design to model basic cognitive processes of a wide range of animals and environments; and (iii) the animat framework enables a uniform and gradual approach to AGI, by successively taking on more challenging problems in the form of broader and more complex classes of environments
{"title":"Learning and decision-making in artificial animals","authors":"Claes Strannegård, Nils Svangård, David Lindström, Joscha Bach, Bas R. Steunebrink","doi":"10.2478/jagi-2018-0002","DOIUrl":"https://doi.org/10.2478/jagi-2018-0002","url":null,"abstract":"Abstract A computational model for artificial animals (animats) interacting with real or artificial ecosystems is presented. All animats use the same mechanisms for learning and decisionmaking. Each animat has its own set of needs and its own memory structure that undergoes continuous development and constitutes the basis for decision-making. The decision-making mechanism aims at keeping the needs of the animat as satisfied as possible for as long as possible. Reward and punishment are defined in terms of changes to the level of need satisfaction. The learning mechanisms are driven by prediction error relating to reward and punishment and are of two kinds: multi-objective local Q-learning and structural learning that alter the architecture of the memory structures by adding and removing nodes. The animat model has the following key properties: (1) autonomy: it operates in a fully automatic fashion, without any need for interaction with human engineers. In particular, it does not depend on human engineers to provide goals, tasks, or seed knowledge. Still, it can operate either with or without human interaction; (2) generality: it uses the same learning and decision-making mechanisms in all environments, e.g. desert environments and forest environments and for all animats, e.g. frog animats and bee animats; and (3) adequacy: it is able to learn basic forms of animal skills such as eating, drinking, locomotion, and navigation. Eight experiments are presented. The results obtained indicate that (i) dynamic memory structures are strictly more powerful than static; (ii) it is possible to use a fixed generic design to model basic cognitive processes of a wide range of animals and environments; and (iii) the animat framework enables a uniform and gradual approach to AGI, by successively taking on more challenging problems in the form of broader and more complex classes of environments","PeriodicalId":247142,"journal":{"name":"Journal of Artificial General Intelligence","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116270048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract This paper presents Unlimited Computable AI, or UCAI, that is a family of computable variants of AIXI. UCAI is more powerful than AIXItl, which is a conventional family of computable variants of AIXI, in the following ways: 1) UCAI supports models of terminating computation, including typed lambda calculi, while AIXItl only supports Turing machine with timeout ˜t, which can be simulated by typed lambda calculi for any ˜t; 2) unlike UCAI, AIXItl limits the program length to some ˜l .
{"title":"Computable Variants of AIXI which are More Powerful than AIXItl","authors":"Susumu Katayama","doi":"10.2478/jagi-2019-0001","DOIUrl":"https://doi.org/10.2478/jagi-2019-0001","url":null,"abstract":"Abstract This paper presents Unlimited Computable AI, or UCAI, that is a family of computable variants of AIXI. UCAI is more powerful than AIXItl, which is a conventional family of computable variants of AIXI, in the following ways: 1) UCAI supports models of terminating computation, including typed lambda calculi, while AIXItl only supports Turing machine with timeout ˜t, which can be simulated by typed lambda calculi for any ˜t; 2) unlike UCAI, AIXItl limits the program length to some ˜l .","PeriodicalId":247142,"journal":{"name":"Journal of Artificial General Intelligence","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124338501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract This paper attempts to replicate the results of evaluating several artificial agents using the Algorithmic Intelligence Quotient test originally reported by Legg and Veness. Three experiments were conducted: One using default settings, one in which the action space was varied and one in which the observation space was varied. While the performance of freq, Q0, Qλ, and HLQλ corresponded well with the original results, the resulting values differed, when using MC-AIXI. Varying the observation space seems to have no qualitative impact on the results as reported, while (contrary to the original results) varying the action space seems to have some impact. An analysis of the impact of modifying parameters of MC-AIXI on its performance in the default settings was carried out with the help of data mining techniques used to identifying highly performing configurations. Overall, the Algorithmic Intelligence Quotient test seems to be reliable, however as a general artificial intelligence evaluation method it has several limits. The test is dependent on the chosen reference machine and also sensitive to changes to its settings. It brings out some differences among agents, however, since they are limited in size, the test setting may not yet be sufficiently complex. A demanding parameter sweep is needed to thoroughly evaluate configurable agents that, together with the test format, further highlights computational requirements of an agent. These and other issues are discussed in the paper along with proposals suggesting how to alleviate them. An implementation of some of the proposals is also demonstrated.
{"title":"Towards General Evaluation of Intelligent Systems: Lessons Learned from Reproducing AIQ Test Results","authors":"Ondrej Vadinský","doi":"10.2478/jagi-2018-0001","DOIUrl":"https://doi.org/10.2478/jagi-2018-0001","url":null,"abstract":"Abstract This paper attempts to replicate the results of evaluating several artificial agents using the Algorithmic Intelligence Quotient test originally reported by Legg and Veness. Three experiments were conducted: One using default settings, one in which the action space was varied and one in which the observation space was varied. While the performance of freq, Q0, Qλ, and HLQλ corresponded well with the original results, the resulting values differed, when using MC-AIXI. Varying the observation space seems to have no qualitative impact on the results as reported, while (contrary to the original results) varying the action space seems to have some impact. An analysis of the impact of modifying parameters of MC-AIXI on its performance in the default settings was carried out with the help of data mining techniques used to identifying highly performing configurations. Overall, the Algorithmic Intelligence Quotient test seems to be reliable, however as a general artificial intelligence evaluation method it has several limits. The test is dependent on the chosen reference machine and also sensitive to changes to its settings. It brings out some differences among agents, however, since they are limited in size, the test setting may not yet be sufficiently complex. A demanding parameter sweep is needed to thoroughly evaluate configurable agents that, together with the test format, further highlights computational requirements of an agent. These and other issues are discussed in the paper along with proposals suggesting how to alleviate them. An implementation of some of the proposals is also demonstrated.","PeriodicalId":247142,"journal":{"name":"Journal of Artificial General Intelligence","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121882212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract One of the essential aspect in biological agents is dynamic stability. This aspect, called homeostasis, is widely discussed in ethology, neuroscience and during the early stages of artificial intelligence. Ashby’s homeostats are general-purpose learning machines for stabilizing essential variables of the agent in the face of general environments. However, despite their generality, the original homeostats couldn’t be scaled because they searched their parameters randomly. In this paper, first we re-define the objective of homeostats as the maximization of a multi-step survival probability from the view point of sequential decision theory and probabilistic theory. Then we show that this optimization problem can be treated by using reinforcement learning algorithms with special agent architectures and theoretically-derived intrinsic reward functions. Finally we empirically demonstrate that agents with our architecture automatically learn to survive in a given environment, including environments with visual stimuli. Our survival agents can learn to eat food, avoid poison and stabilize essential variables through theoretically-derived single intrinsic reward formulations.
{"title":"Homeostatic Agent for General Environment","authors":"N. Yoshida","doi":"10.1515/jagi-2017-0001","DOIUrl":"https://doi.org/10.1515/jagi-2017-0001","url":null,"abstract":"Abstract One of the essential aspect in biological agents is dynamic stability. This aspect, called homeostasis, is widely discussed in ethology, neuroscience and during the early stages of artificial intelligence. Ashby’s homeostats are general-purpose learning machines for stabilizing essential variables of the agent in the face of general environments. However, despite their generality, the original homeostats couldn’t be scaled because they searched their parameters randomly. In this paper, first we re-define the objective of homeostats as the maximization of a multi-step survival probability from the view point of sequential decision theory and probabilistic theory. Then we show that this optimization problem can be treated by using reinforcement learning algorithms with special agent architectures and theoretically-derived intrinsic reward functions. Finally we empirically demonstrate that agents with our architecture automatically learn to survive in a given environment, including environments with visual stimuli. Our survival agents can learn to eat food, avoid poison and stabilize essential variables through theoretically-derived single intrinsic reward formulations.","PeriodicalId":247142,"journal":{"name":"Journal of Artificial General Intelligence","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115719964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Claes Strannegård, Abdul Rahim Nizamani, Jonas Juel, U. Persson
Abstract In the story Alice in Wonderland, Alice fell down a rabbit hole and suddenly found herself in a strange world called Wonderland. Alice gradually developed knowledge about Wonderland by observing, learning, and reasoning. In this paper we present the system Alice In Wonderland that operates analogously. As a theoretical basis of the system, we define several basic concepts of logic in a generalized setting, including the notions of domain, proof, consistency, soundness, completeness, decidability, and compositionality. We also prove some basic theorems about those generalized notions. Then we model Wonderland as an arbitrary symbolic domain and Alice as a cognitive architecture that learns autonomously by observing random streams of facts from Wonderland. Alice is able to reason by means of computations that use bounded cognitive resources. Moreover, Alice develops her belief set by continuously forming, testing, and revising hypotheses. The system can learn a wide class of symbolic domains and challenge average human problem solvers in such domains as propositional logic and elementary arithmetic.
{"title":"Learning and Reasoning in Unknown Domains","authors":"Claes Strannegård, Abdul Rahim Nizamani, Jonas Juel, U. Persson","doi":"10.1515/jagi-2016-0002","DOIUrl":"https://doi.org/10.1515/jagi-2016-0002","url":null,"abstract":"Abstract In the story Alice in Wonderland, Alice fell down a rabbit hole and suddenly found herself in a strange world called Wonderland. Alice gradually developed knowledge about Wonderland by observing, learning, and reasoning. In this paper we present the system Alice In Wonderland that operates analogously. As a theoretical basis of the system, we define several basic concepts of logic in a generalized setting, including the notions of domain, proof, consistency, soundness, completeness, decidability, and compositionality. We also prove some basic theorems about those generalized notions. Then we model Wonderland as an arbitrary symbolic domain and Alice as a cognitive architecture that learns autonomously by observing random streams of facts from Wonderland. Alice is able to reason by means of computations that use bounded cognitive resources. Moreover, Alice develops her belief set by continuously forming, testing, and revising hypotheses. The system can learn a wide class of symbolic domains and challenge average human problem solvers in such domains as propositional logic and elementary arithmetic.","PeriodicalId":247142,"journal":{"name":"Journal of Artificial General Intelligence","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134121636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Sigma (Σ) is a cognitive architecture and system whose development is driven by a combination of four desiderata: grand unification, generic cognition, functional elegance, and sufficient efficiency. Work towards these desiderata is guided by the graphical architecture hypothesis, that key to progress on them is combining what has been learned from over three decades’ worth of separate work on cognitive architectures and graphical models. In this article, these four desiderata are motivated and explained, and then combined with the graphical architecture hypothesis to yield a rationale for the development of Sigma. The current state of the cognitive architecture is then introduced in detail, along with the graphical architecture that sits below it and implements it. Progress in extending Sigma beyond these architectures and towards a full cognitive system is then detailed in terms of both a systematic set of higher level cognitive idioms that have been developed and several virtual humans that are built from combinations of these idioms. Sigma as a whole is then analyzed in terms of how well the progress to date satisfies the desiderata. This article thus provides the first full motivation, presentation and analysis of Sigma, along with a diversity of more specific results that have been generated during its development.
{"title":"The Sigma Cognitive Architecture and System: Towards Functionally Elegant Grand Unification","authors":"P. Rosenbloom, A. Demski, Volkan Ustun","doi":"10.1515/JAGI-2016-0001","DOIUrl":"https://doi.org/10.1515/JAGI-2016-0001","url":null,"abstract":"Abstract Sigma (Σ) is a cognitive architecture and system whose development is driven by a combination of four desiderata: grand unification, generic cognition, functional elegance, and sufficient efficiency. Work towards these desiderata is guided by the graphical architecture hypothesis, that key to progress on them is combining what has been learned from over three decades’ worth of separate work on cognitive architectures and graphical models. In this article, these four desiderata are motivated and explained, and then combined with the graphical architecture hypothesis to yield a rationale for the development of Sigma. The current state of the cognitive architecture is then introduced in detail, along with the graphical architecture that sits below it and implements it. Progress in extending Sigma beyond these architectures and towards a full cognitive system is then detailed in terms of both a systematic set of higher level cognitive idioms that have been developed and several virtual humans that are built from combinations of these idioms. Sigma as a whole is then analyzed in terms of how well the progress to date satisfies the desiderata. This article thus provides the first full motivation, presentation and analysis of Sigma, along with a diversity of more specific results that have been generated during its development.","PeriodicalId":247142,"journal":{"name":"Journal of Artificial General Intelligence","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133159343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract As generative AI systems grow in sophistication, so too do our expectations of their outputs. For as automated systems acculturate themselves to ever larger sets of inspiring human examples, the more we expect them to produce human-quality outputs, and the greater our disappointment when they fall short. While our generative systems must embody some sense of what constitutes human creativity if their efforts are to be valued as creative by human judges, computers are not human, and need not go so far as to actively pretend to be human to be seen as creative. As discomfiting objects that reside at the boundary of two seemingly disjoint categories, creative machines arouse our sense of the uncanny, or what Freud memorably called the Unheimlich. Like a ventriloquist’s doll that finds its own voice, computers are free to blend the human and the non-human, to surprise us with their knowledge of our world and to discomfit with their detached, other-worldly perspectives on it. Nowhere is our embrace of the unnatural and the uncanny more evident than in the popularity of Twitterbots, automatic text generators on Twitter that are followed by humans precisely because they are non-human, and because their outputs so often seem meaningful yet unnatural. This paper evaluates a metaphor generator named @MetaphorMagnet, a Twitterbot that tempers the uncanny with aptness to yield results that are provocative but meaningful.
{"title":"Unnatural Selection: Seeing Human Intelligence in Artificial Creations","authors":"T. Veale","doi":"10.1515/jagi-2015-0002","DOIUrl":"https://doi.org/10.1515/jagi-2015-0002","url":null,"abstract":"Abstract As generative AI systems grow in sophistication, so too do our expectations of their outputs. For as automated systems acculturate themselves to ever larger sets of inspiring human examples, the more we expect them to produce human-quality outputs, and the greater our disappointment when they fall short. While our generative systems must embody some sense of what constitutes human creativity if their efforts are to be valued as creative by human judges, computers are not human, and need not go so far as to actively pretend to be human to be seen as creative. As discomfiting objects that reside at the boundary of two seemingly disjoint categories, creative machines arouse our sense of the uncanny, or what Freud memorably called the Unheimlich. Like a ventriloquist’s doll that finds its own voice, computers are free to blend the human and the non-human, to surprise us with their knowledge of our world and to discomfit with their detached, other-worldly perspectives on it. Nowhere is our embrace of the unnatural and the uncanny more evident than in the popularity of Twitterbots, automatic text generators on Twitter that are followed by humans precisely because they are non-human, and because their outputs so often seem meaningful yet unnatural. This paper evaluates a metaphor generator named @MetaphorMagnet, a Twitterbot that tempers the uncanny with aptness to yield results that are provocative but meaningful.","PeriodicalId":247142,"journal":{"name":"Journal of Artificial General Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115693729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Image schemas are recognised as a fundamental ingredient in human cognition and creative thought. They have been studied extensively in areas such as cognitive linguistics. With the goal of exploring their potential role in computational creative systems, we here study the viability of the idea to formalise image schemas as a set of interlinked theories. We discuss in particular a selection of image schemas related to the notion of ‘path’, and show how they can be mapped to a formalised family of microtheories reflecting the different aspects of path following. Finally, we illustrate the potential of this approach in the area of concept invention, namely by providing several examples illustrating in detail in what way formalised image schema families support the computational modelling of conceptual blending.
{"title":"Choosing the Right Path: Image Schema Theory as a Foundation for Concept Invention","authors":"Maria M. Hedblom, O. Kutz, F. Neuhaus","doi":"10.1515/jagi-2015-0003","DOIUrl":"https://doi.org/10.1515/jagi-2015-0003","url":null,"abstract":"Abstract Image schemas are recognised as a fundamental ingredient in human cognition and creative thought. They have been studied extensively in areas such as cognitive linguistics. With the goal of exploring their potential role in computational creative systems, we here study the viability of the idea to formalise image schemas as a set of interlinked theories. We discuss in particular a selection of image schemas related to the notion of ‘path’, and show how they can be mapped to a formalised family of microtheories reflecting the different aspects of path following. Finally, we illustrate the potential of this approach in the area of concept invention, namely by providing several examples illustrating in detail in what way formalised image schema families support the computational modelling of conceptual blending.","PeriodicalId":247142,"journal":{"name":"Journal of Artificial General Intelligence","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127848910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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