By Edwina L. Rissland (auth.), Thomas R. Roth-Berghofer, Mehmet H. Göker, H. Altay Güvenir (eds.)
This e-book constitutes the refereed lawsuits of the eighth eu convention on Case-Based Reasoning, ECCBR 2004, held in Fethiye, Turkey in September 2006.
The 31 revised complete papers and five revised program papers provided including 2 invited papers and a couple of abstracts of invited talks have been rigorously reviewed and chosen from a variety of submissions. All present matters in case-based reasoning, starting from theoretical and methodological matters to complicated functions in numerous fields are addressed, hence offering a consultant picture of present CBR research.
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Extra info for Advances in Case-Based Reasoning: 8th European Conference, ECCBR 2006 Fethiye, Turkey, September 4-7, 2006 Proceedings
1898. Springer-Verlag, Berlin Heidelberg New York (2000) 49-60 23. : A Similarity-Based Approach to Attribute Selection in User-Adaptive Sales Dialogues. , Watson, I. ): Case-Based Reasoning Research and Development. LNAI, Vol. 2080. Springer-Verlag, Berlin Heidelberg New York (2001) 306-320 24. : Minimizing Dialog Length in Interactive Case-Based Reasoning. Proceedings of the 17th International Joint Conference on Artificial Intelligence (2001) 993-998 25. : Increasing Dialogue Efficiency in Case-Based Reasoning without Loss of Solution Quality.
A research issue. – And last but not least, if the Semantic Web will play a role in the future, then what is the role of CBR in the Semantic Web? The latter, sounding academic again, is an issue for the CBR research community, since it asks the question for future directions. From realities perspective this could become an important one. R. Roth-Berghofer et al. ): ECCBR 2006, LNAI 4106, p. 31, 2006. de Abstract. In both research ﬁelds, Case-Based Reasoning and Reinforcement Learning, the system under consideration gains its expertise from experience.
For the case of ﬁnite state and action spaces where the Q function can be represented using a look-up table, there are convergence guarantees that say that Q learning converges to the optimal value function Q , assumed that all state-action pairs are visited inﬁnitely often and that α diminishes appropriately. Given convergence to Q , the optimal policy π can be induced by greedy exploitation of Q according to π (s) = arg maxa∈A(s) Q (s, a). If there are multiple agents acting concurrently, actions are vectors of individual actions.