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 sub-cases. We deal with issues involved in learning and exploiting the learned knowledge in multi-agent case-based systems. We propose a novel algorithm called OA*, which composes optimal overall cases from distributed case components, and prove its optimality. We then experiment with OA* in a transportation domain on a grid world. We provide empirical results that provide strong evidence of the effectiveness of OA* for the distributed case-based learning task