Cautious Cooperative Learning with Distributed Case-Based Reasoning

In this paper, we propose a cautious cooperative learning ap-proach using distributed case-based reasoning. Our approach consists of two learning mechanisms: individual and coopera-tive learning. Normally, an agent conducts individual learn-ing to learn from its past behavior. When the agent encoun-ters a problem that it has failed to solve (satisfactorily), it triggers cooperative learning, asking for help from its neighboring agents. To avoid corrupting its own casebase and incurring costs on itself and other agents, our agent em-ploys an axiomatic, cautious strategy that includes the notion of a chronological casebase, a profile-based neighbor selec-tion, and a case review and adaptation before adopting an in-coming case. Here we report on the approach and some re-sults in a real-time negotiation domain