Abstract
In the last few years we have witnessed increased popularity of agent systems. This popularity is the result of agents’ ability to work effectively and perform complex tasks in a wide range of applications. In this paper, we highlight the importance of learning mechanisms that are essential for behavioural adaptation of agents in complex environments. We provide a high-level introduction and overview of different types of learning approaches proposed in recent years. We also argue the necessity of dynamic learning processes for handling uncertainty, and propose an uncertainty-oriented architecture of agents together with a specialized knowledge base.
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References
Hayes, C.C.: Agents in a nutshell-a very brief introduction. IEEE Transactions on Knowledge and Data Engineering 11, 127–132 (1999)
Edwards, P.: Intelligent agents=learning agents. In: UK Intelligent Agents Workshop, pp. 157–161. SGES Publications, Oxford (1997)
Demiris, Y., Meltzoff, A.: The robot in the crib: a developemental analysis of imitation skills in infants and robots. Infant. Child Dev. 17, 43–53 (2008)
Ramamurthy, U., Negatu, A., Franklin, S.: Learning mechanisms for intelligent systems. In: International Conference on Advances in Infrastructure for Electronic Business, Science, and Education on the Internet (SSGRR 2001), Italy, (2001)
Lemouzy, S., Camps, V., Glize, P.: Towards a self-organising mechanism for learning adaptive decision-making rules. In: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2008, pp. 616–620 (2008)
Brooks, R.: A robust layered control system for a mobile robot. IEEE Journal of Robotics and Automation 2, 14–23 (1986)
Asgharbeygi, N., Nejati, N., Langley, P., Arai, S.: Guiding Inference Through Relational Reinforcement Learning. In: Kramer, S., Pfahringer, B. (eds.) ILP 2005. LNCS (LNAI), vol. 3625, pp. 20–37. Springer, Heidelberg (2005)
Dzeroski, S., De Raedt, L., Blockeel, H.: Relational reinforcement learning. In: Proceedings of the Fifteenth International Conference on Machine Learning, pp. 136–143 (1998)
Doyle, J.: A truth maintenance system. Artif. Intell. 12, 231–272 (1979)
Yager, R.R.: A model of participatory learning. IEEE Transactions on Systems, Man and Cybernetics 20, 1229–1234 (1990)
Brown, R.G.: Smoothing, Forecasting and Prediction of Discrete Time Series. Prentice Hall, New Jersey (1963)
Doctor, F., Hagras, H., Callaghan, V., Lopez, A.: An adaptive fuzzy learning mechanism for intelligent agents in ubiquitous computing environments. In: Proceedings of the Automation Congress World, pp. 101–106 (2004)
Castellano, G., Fanelli, A.M., Mencar, C.: Generation of interpretable fuzzy granules by a double-clustering technique. Archive of Control Sciences, Special Issue on Granular Computing 12, 397–410 (2002)
Wang, L., Mendel, J.M.: Generating fuzzy rules by learning from examples. In: Proceedings of the 1991 IEEE International Symposium on Intelligent Control, pp. 263–268 (1991)
Crandall, J.W., Goodrich, M.A., Lin, L.: Encoding intelligent agents for uncertain, unknown, and dynamic tasks: From programming to interactive artificial learning. In: AAAI Spring Symposium: Agents that Learn from Human Teachers, pp. 28–35 (2009)
Hamidi, M., Fijany, A., Fontaine, J.: Enhancing inference in relational reinforce-ment learning via truth maintenance systems. In: The Ninth International Conference on Machine Learning and Applications (ICMLA 2010), pp. 407–413 (2010)
Richardson, M., Agrawal, R., Domingos, P.: Trust Management for the Semantic Web. In: Fensel, D., Sycara, K., Mylopoulos, J. (eds.) ISWC 2003. LNCS, vol. 2870, pp. 351–368. Springer, Heidelberg (2003)
O’Hara, K., Alani, H., Kalfoglou, Y., Shadbolt, N.: Trust Strategies for the Semantic Web. In: McIlraith, S.A., Plexousakis, D., van Harmelen, F. (eds.) ISWC 2004. LNCS, vol. 3298, pp. 78–85. Springer, Heidelberg (2004)
Horrocks, I., Parsia, B., Patel-Schneider, P.F., Hendler, J.: Semantic Web Architecture: Stack or Two Towers? In: Fages, F., Soliman, S. (eds.) PPSWR 2005. LNCS, vol. 3703, pp. 37–41. Springer, Heidelberg (2005)
Mccauley-Bell, P.: Intelligent agent characterization and uncertainty management with fuzzy set theory: a tool to support early supplier integration. Journal of Intelligent Manufacturing 10, 135–147 (1999)
Russell, S.J.: Norvig, Artificial Intelligence: A Modern Approach. Prentice Hall (2003)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann (1997)
Dubois, D., Lang, J., Prade, H.: A Brief Review of Possibilistic Logic. In: Kruse, R., Siegel, P. (eds.) ECSQAU 1991 and ECSQARU 1991. LNCS, vol. 548, pp. 53–57. Springer, Heidelberg (1991)
Resnik, M.D.: Choices: An Introduction to Decision Theory. University of Minnestoa Press, Minneapolis (1987)
Fudenberg, D., Tirole, J.: Game Theory. MIT Press, Cambridge (1992)
Rosenschein, S., Zlotkin, G.: Rules of Encounter: Designing Conventions for Automated Negotiation among Computers. MIT Press, Cambridge (1994)
Reusser, D.E., Hare, M., Paul-Wostl, C.: Relating choice of agent rationality to agent model uncertainty - an experimental study. In: iIEMSs Complexity and Integrated Resources Management (2004)
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Zadeh, P.D.H., Reformat, M.Z. (2013). Learning Techniques in Presence of Uncertainty. In: Yager, R., Abbasov, A., Reformat, M., Shahbazova, S. (eds) Soft Computing: State of the Art Theory and Novel Applications. Studies in Fuzziness and Soft Computing, vol 291. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34922-5_10
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DOI: https://doi.org/10.1007/978-3-642-34922-5_10
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