Abstract
Multiagent systems offer a new paradigm to organize AI applications. Our goal is to develop techniques to integrate CBR into applications that are developed as multiagent systems. CBR offers the multiagent systems paradigm the capability of autonomously learning from experience. In this paper we present a framework for collaboration among agents that use CBR and some experiments illustrating the framework. We focus on three collaboration policies for CBR agents: Peer Counsel, Bounded Counsel and Committee policies. The experiments show that the CBR agents improve their individual performance collaborating with other agents without compromising the privacy of their own cases. We analyze the three policies concerning accuracy, cost, and robustness with respect to number of agents and case base size.
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Plaza, E., OntaÑón, S. (2001). Ensemble Case-Based Reasoning: Collaboration Policies for Multiagent Cooperative CBR. In: Aha, D.W., Watson, I. (eds) Case-Based Reasoning Research and Development. ICCBR 2001. Lecture Notes in Computer Science(), vol 2080. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44593-5_31
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DOI: https://doi.org/10.1007/3-540-44593-5_31
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