Case exchange strategies in multiagent learning

Abstract

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 retain where the agents take in consideration that they are not learning in isolation but in a multiagent system. We also present case bartering as an effective strategy when the agents have a biased view of the data. The outcome of both case retain and bartering is an improvement of individual agent performance and overall multiagent system performance. We also present empirical results comparing all the strategies proposed.