Abstract
An intelligent agent situated in an environment needs to know the preferred states it is expected to achieve or maintain so that it can work towards achieving or maintaining them. We refer to all these preferred states as “preferences”. The preferences an agent has selected to bring about at a given time are called “goals”. This selection of preferences as goals is generally referred to as “goal generation”. Basic aim behind goal generation is to provide the agent with a way of getting new goals. Although goal generation results in an increase in the agent’s knowledge about its goals, the overall autonomy of the agent does not increase as its goals are derived from its preferences (which are programmed). We argue that to achieve greater autonomy, an agent must be able to generate new preferences. In this paper we discuss how an agent can generate new preferences based on analogy between new objects and the objects it has known preferences for.
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References
Aha, D.W., Kibler, D., Albert, M.K.: Instance-based learning algorithms. Machine Learning 6(1), 37–66 (1991)
Bratman, M.: Intention, plans, and practical reason. Harvard University Press, Cambridge (1987)
Broersen, J., Dastani, M., Hulstijn, J., van der Torre, L.: Goal generation in the BOID architecture. Cognitive Science Quarterly 2(3-4), 428–447 (2002)
Clement, B.J., Durfee, E.H.: Theory for coordinating concurrent hierarchical planning agents using summary information. In: Proceedings of AAAI, pp. 495–502 (1999)
Cover, T., Hart, P.E.: Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13(1), 21–27 (1967)
da Costa Pereira, C., Tettamanzi, A.G.B.: Goal generation with relevant and trusted beliefs. In: Proceedings of AAMAS’08, pp. 397–404 (2008)
Dignum, F., Kinny, D.: From desires, obligations and norms to goals. Cognitive Science Quarterly 2 (2002)
Maslow, A.: Motivation and Personality. Harper & Row, New York (1954)
Rafique, U., Huang, S.Y.: A new action description scheme for informal reasoning. In: Arabnia, H.R., de la Fuente, D., Olivas, J.A. (eds.) Proceedings of ICAI’09, vol. II, pp. 582–588 (2009)
Reiss, S.: Multifaceted nature of intrinsic motivation: The theory of 16 basic desires. Review of General Psychology 8(3), 179–193 (2004)
Simon, H.: Motivational and emotional controls of cognition. Psychological Review 74(1), 29–39 (1967)
Simpson, R., Schreckenghost, D., LoPresti, E., Kirsch, N.: Plans and planning in smart homes. In: Augusto, J.C., Nugent, C.D. (eds.) Designing Smart Homes. LNCS (LNAI), vol. 4008, pp. 71–84. Springer, Heidelberg (2006)
Thangarajah, J., Harland, J., Yorke-Smith, N.: A soft COP model for goal deliberation in a BDI agent. In: CP’07 Workshop on Constraint Modelling and Reformulation (2007)
Thangarajah, J., Padgham, L., Harland, J.: Representation and reasoning for goals in BDI agents. Australian Computer Science Communications 24(1), 259–265 (2002)
Thangarajah, J., Padgham, L., Winikoff, M.: Detecting & exploiting positive goal interaction in intelligent agents. In: Proceedings of AAMAS’03, pp. 401–408. ACM, New York (2003)
Thangarajah, J., Padgham, L., Winikoff, M.: Detecting and avoiding interference between goals in intelligent agents. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp. 721–726. Academic Press, London (2003)
Thomason, R.H.: Desires and defaults: A framework for planning with inferred goals. In: Proceedings of KR 2000, pp. 702–713 (2000)
Birna van Riemsdijk, M., Dastani, M., Winikoff, M.: Goals in agent systems: a unifying framework. In: Proceedings of AAMAS’08, pp. 713–720 (2008)
Wettschereck, D., Aha, D.W., Mohri, T.: A review and empirical evaluation of feature weighting methods for a class of lazy learning algorithms. Artificial Intelligence Review 11, 273–314 (1997)
Randall Wilson, D., Martinez, T.R.: Improved heterogeneous distance functions. Journal of Artificial Intelligence Research 6, 1–34 (1997)
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Rafique, U., Huang, S.Y. (2010). Preference Generation for Autonomous Agents. In: Dix, J., Witteveen, C. (eds) Multiagent System Technologies. MATES 2010. Lecture Notes in Computer Science(), vol 6251. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16178-0_17
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DOI: https://doi.org/10.1007/978-3-642-16178-0_17
Publisher Name: Springer, Berlin, Heidelberg
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