Taking as biological inspiration the António Damásioproposal that the brain emotion mechanisms are essential forappropriate decision-making, a conceptual model for an autonomousagent based on a double-representation paradigm ispresented. Stimuli is represented under two distinct perspectives, thusinducing two representation schemata with different properties.The consequences of this model are explored in various forms. First,the applicability of the model to anticipation, and to the formulation ofcausal models about the world are explored. And second, a formalapproach is presented, where theoretical consequences arederived, first from a probabilistic standpoint, followed by an approachbased on the assumption that the above-mentioned representationslive in metric spaces. Following this latter approach, an algorithmis proposed to adapt the metric for one of the spaces, as well as toprovide a guidance for the improvement of that representation,aiming at the creation of new features. The formulation of thisalgorithm is based on Multidimensional Scaling techniques.
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Taking as biological inspiration the António Damásioproposal that the brain emotion mechanisms are essential forappropriate decision-making, a conceptual model for an autonomousagent based on a double-representation paradigm ispresented. Stimuli is represented under two distinct perspectives, thusinducing two representation schemata with different properties.The consequences of this model are explored in various forms. First,the applicability of the model to anticipation, and to the formulation ofcausal models about the world are explored. And second, a formalapproach is presented, where theoretical consequences arederived, first from a probabilistic standpoint, followed by an approachbased on the assumption that the above-mentioned representationslive in metric spaces. Following this latter approach, an algorithmis proposed to adapt the metric for one of the spaces, as well as toprovide a guidance for the improvement of that representation,aiming at the creation of new features. The formulation of thisalgorithm is based on Multidimensional Scaling techniques.
Rodrigo Ventura received his PhD (2008) in ECE from InstitutoSuperior Técnico (IST, Technical University of Lisbon), Portugal. He is an AssistantProfessor at IST and member of Institute for Systems and Robotics (ISR). CarlosPinto-Ferreira received his PhD (1991) in ECE from IST. He is Associate Professor at IST and member of ISR.
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Taschenbuch. Zustand: Neu. Emotion-based Mechanisms for Decision Making in Autonomous Agents | Conceptual model and experimental results | Rodrigo Ventura (u. a.) | Taschenbuch | 224 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783844307641 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. Artikel-Nr. 106922419
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Taschenbuch. Zustand: Neu. Neuware -Taking as biological inspiration the António Damásioproposal that the brain emotion mechanisms are essential forappropriate decision-making, a conceptual model for an autonomousagent based on a double-representation paradigm ispresented. Stimuli is represented under two distinct perspectives, thusinducing two representation schemata with different properties.The consequences of this model are explored in various forms. First,the applicability of the model to anticipation, and to the formulation ofcausal models about the world are explored. And second, a formalapproach is presented, where theoretical consequences arederived, first from a probabilistic standpoint, followed by an approachbased on the assumption that the above-mentioned representationslive in metric spaces. Following this latter approach, an algorithmis proposed to adapt the metric for one of the spaces, as well as toprovide a guidance for the improvement of that representation,aiming at the creation of new features. The formulation of thisalgorithm is based on Multidimensional Scaling techniques.Books on Demand GmbH, Überseering 33, 22297 Hamburg 224 pp. Englisch. Artikel-Nr. 9783844307641
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