On retrieval and reasoning in distributed case bases

Distributed AI (DAI) systems exploiting CBR techniques have to deal with the problem of retrieving episodes which 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. Each of the agent's partial view may result in local cases that are best matches based on the local view. However, these local cases when assembled may not result in the best overall case in terms of global measures. We propose a negotiation-driven case retrieval algorithm as an approach to dynamically resolving inconsistencies between different case pieces during the retrieval process. Agents augment each other's view with nonlocal information to the extent that a good overall episode is formed from the integration of locally retrieved cases.

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