A bartering approach to improve multiagent learning

摘要

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 that address the issue of agents having a biased view of the data. The outcome of bartering is an improvement of individual agent performance and of overall multiagent system performance that equals the ideal situation where all agents have an unbiased view of the data. We also present empirical results illustrating the robustness of the case bartering process for several configurations of the multiagent system and for three different CBR techniques.