Alle Publikationen
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2007
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(2007) : The question of ’what to imitate’: Inferring goals and intentions from demonstrations In: Nehaniv, Chrystopher L.; Dautenhahn, Kerstin (Hg.): Imitation and social learning in robots, humans and animals: Behavioural, social and communicative dimensions: New York, NY: Cambridge University Press, S. 135-151
DOI: https://doi.org/10.1017/CBO9780511489808.011 Abstract: A difficult issue for robotics researchers is the question of what to imitate, that is, which aspects of a demonstration robots should copy. Sometimes it is appropriate to copy others’ actions, sometimes it is appropriate to copy others’ results and sometimes copying both or even neither of these is the most appropriate response. The chapter discusses the advantages of using an understanding of others’ goals and intentions to answer this question, copying what the demonstrator intended to do rather than what he actually did. Whereas some animals focus mainly on demonstrators’ results or actions, one-year-old human infants appear to use an understanding of others’ goals to decide what to imitate. The authors identify some specific ways in which infants can infer the goal of a demonstrator in imitation situations, using such information as the demonstrators’ gaze direction, emotional expressions, actions and the context. This is followed by a brief review of what robots currently can do in this regard, proposing some further challenges for them. (PsycINFO Database Record (c) 2019 APA, all rights reserved)
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(2007): Social Systems. In: Philosophical Psychology 20 (5), S. 557-578. DOI: 10.1080/09515080701545981
Abstract: It used to be thought that folk psychology is the only game in town. Focusing merely on what people do will not allow you to predict what they are likely to do next. For that, you must consider their beliefs, desires, intentions, etc. Recent evidence from developmental psychology and fMRI studies indicates that this conclusion was premature. We parse motion in an environment as behavior of a particular type, and behavior thus construed can feature in systematizations that we know. Building on the view that folk psychological knowledge is knowledge of theoretical models, I argue that social knowledge is best understood as lying on a continuum between behavioral and full-blown psychological models. Between the two extremes, we have what I call social models. Social models represent social structures in terms of their overall purpose and circumscribe individuals' roles within them. These models help us predict what others will do or plan what we should do without providing information about what agents think or want. Thinking about social knowledge this way gives us a more nuanced picture of what capacities are engaged in social planning and interaction, and gives us a better tool with which to think about the social knowledge of animals and young children.
Keywords: Agenten-Überzeugung, BELIEFS, Beliefs in beliefs, Disziplin, Entwicklungspsychologie, Erwartungen, Erwartungserwartung, folk psychology, INFANTS, Intellektualtechnik, INTENTIONS, Interaktion, KNOWLEDGE, Mensch-Technik-Relationen (MTR), MIND, Modelle/Theorien, ORIGINS, OTHERS, PERCEPTION, Philosophical Psychology, Philosophie, philosophy of mind, Psychologie, REPRESENTATION, social knowledge, RHESUS-MONKEYS, social systems, ethics, soziale Kognition, Sozialverhalten, Sozialverstehen, Technik, theoretical models, theory of mind, Überzeugung 1999
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(1999) : The Belief-Desire-Intention Model of Agency In: Goos, Gerhard; Hartmanis, Juris; van Leeuwen, Jan; Müller, Jörg P.; Rao, Anand S.; Singh, Munindar P. (Hg.): Intelligents agents V: Agent theories, architectures, and languages : 5th International Workshop, ATAL'98 : Paris, France, July 1998 : proceedings, 1555: New York: Springer (Lecture notes in computer science Lecture notes in artificial intelligence), S. 1-10
DOI: https://doi.org/10.1007/3-540-49057-4_1 Abstract: Within the ATAL community, the belief-desire-intention (BDI) model has come to be possibly the best known and best studied model of practical reasoning agents. There are several reasons for its success, but perhaps the most compelling are that the BDI model combines a respectable philosophical model of human practical reasoning, (originally developed by Michael Bratman [1]), a number of implementations (in the IRMA architecture [2] and the various PRS-like systems currently available [7]), several successful applications (including the now-famous fault diagnosis system for the space shuttle, as well as factory process control systems and business process management [8]), and finally, an elegant abstract logical semantics, which have been taken up and elaborated upon widely within the agent research community [14, 16].
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