Distributed Case-Based Learning

Multi-agent systems exploiting case based reasoning techniques have to deal with the problem of retrieving episodes that are themselves distributed across a set of agents. From a Gestalt perspective, a good overall case may not be the one derived from the summation of best subcases. In this paper we deal with issues involved in learning and exploiting the learned knowledge in multi-agent case-based systems