By Edwina L. Rissland (auth.), Thomas R. Roth-Berghofer, Mehmet H. Göker, H. Altay Güvenir (eds.)
This ebook constitutes the refereed court cases 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 software papers awarded including 2 invited papers and a couple of abstracts of invited talks have been rigorously reviewed and chosen from quite a few submissions. All present concerns in case-based reasoning, starting from theoretical and methodological matters to complicated functions in a variety of fields are addressed, hence proposing a consultant photo of present CBR research.
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Additional info for Advances in Case-Based Reasoning: 8th European Conference, ECCBR 2006 Fethiye, Turkey, September 4-7, 2006 Proceedings
We evaluate our algorithms in a case study in reactive production scheduling. 1 Introduction A reinforcement learning (RL) agent must acquire its behavior policy by repeatedly collecting experience within its environment. Usually, that experience is then processed into a state or state-action value function, from which an appropriate behaviour policy can be induced easily . When applying RL approaches to complex and/or real-world domains, typically some kind of function approximation mechanism to represent the value function has to be used.
Q may be updated appropriately. Index Selection Harmonisation: The procedure to select index x within an experience list Ei (s) must be implemented in exactly the same manner in every agent. It has to guarantee that at each instant of time each agent selects the same index, which implies that all agents must be aware of the same time. Eﬃcient Exploration: Due to the sorting of all experience lists, the best actions are to be found at their beginnings. Therefore, an index selection mechanism that aims at greedily exploiting the knowledge contained in its lists, would always select the ﬁrst index.
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 . In this paper, we focus on independent learners: As argued before, the key problem of independent reinforcement learners is that they must somehow be enabled to distinguish between diﬀerent joint actions to which they contributed the same elementary action.