Cooperative Case Bartering for Case-Based Reasoning Agents

Multiagent systems offer a new paradigm to organize AI Applications. We focus on the application of Case-Based Reasoning to Multiagent systems. CBR offers the individual agents the capability of autonomously learn from experience. In this paper we present a framework for collaboration among agents that use CBR. We present explicit strategies for case bartering in order improve individual case bases and reduce bias in the case bases. We also present empirical results illustrating the robustness of the case bartering process for several configurations of the multiagent system. Finally, a bias and variance analysis of the effects of bartering is included.