By Edwina L. Rissland (auth.), Thomas R. Roth-Berghofer, Mehmet H. Göker, H. Altay Güvenir (eds.)
This ebook constitutes the refereed complaints of the eighth ecu convention on Case-Based Reasoning, ECCBR 2004, held in Fethiye, Turkey in September 2006.
The 31 revised complete papers and five revised program papers awarded including 2 invited papers and a pair of abstracts of invited talks have been conscientiously reviewed and chosen from various submissions. All present concerns in case-based reasoning, starting from theoretical and methodological concerns to complex purposes in quite a few fields are addressed, therefore proposing a consultant image of present CBR research.
Read Online or Download Advances in Case-Based Reasoning: 8th European Conference, ECCBR 2006 Fethiye, Turkey, September 4-7, 2006 Proceedings PDF
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Extra resources for Advances in Case-Based Reasoning: 8th European Conference, ECCBR 2006 Fethiye, Turkey, September 4-7, 2006 Proceedings
31, 2006. de Abstract. In both research ﬁelds, Case-Based Reasoning and Reinforcement Learning, the system under consideration gains its expertise from experience. Utilizing this fundamental common ground as well as further characteristics and results of these two disciplines, in this paper we develop an approach that facilitates the distributed learning of behaviour policies in cooperative multi-agent domains without communication between the learning agents. We evaluate our algorithms in a case study in reactive production scheduling.
BG is incomplete regardless of the size of the retrieval set or number of candidate cases from which the retrieved cases are selected. Proof. For any values of the parameters b and r, BG selects r cases from the k-NN retrieval set for k = b × r. Its incompleteness immediately follows from Theorem 6. A feature that BG shares with k-NN is that the most similar case for a given query is always retrieved. 6 Diversity Conscious Retrieval Usually diversity can be increased only at the expense of some loss of average similarity relative to the k-NN retrieval set.
A mapping from states to actions, that maximises the sum of expected rewards . In  the distinction between joint-action learners and independent learners is introduced. While the former know about the actions taken by the other agents, the latter only know about their own contribution ai to the joint action. As a consequence of their lack of action information, the attempt to estimate an elementary action’s value in a speciﬁc state would most likely fail: The reward signals for diﬀerent joint action vectors (diﬀering in the action contributions of the other agents) would mix for any state s and any elementary action ai .