Fuzzy Measure Theory
Cooperative Case-Based Reasoning
ECAI '96 Selected papers from the Workshop on Distributed Artificial Intelligence Meets Machine Learning, Learning in Multi-Agent Environments
Modelling the Competence of Case-Bases
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
Recent Literature Collected by Didier DUBOIS, Henri PRADE and Salvatore SESSA
Fuzzy Sets and Systems
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In a collaborative (distributed) Case-Based Reasoning (CBR) environment, an input query case could be compared with the old cases that are resided in many different CBR agents in the network. How to obtain the best solution effectively and efficiently from this distributed CBR network depends on a carefully designed query dispatching strategy. In this paper, we propose a fuzzy integral based approach to measure the competence of different CBR agents in the network and suggest three query dispatching policies which could be used to fulfill this task. They are: To-Top policy, Strong-Strong policy and Best-Committee policy. The experimental result shows that our proposed policies are comparatively better than the existing ones developed by Plaza and Ontañón.