Résumé (EN)

What happens when distributed sources of information (agents) hold and acquire information locally, and have to communicate with neighbouring agents in order to refine their hypothesis regarding the actual global state of this environment? This question occurs when it is not possible (e. g. for practical or privacy concerns) to collect observations and knowledge, and centrally compute the resulting theory. In this paper, we assume that agents are equipped with full clausal theories and individually face abductive tasks, in a globally consistent environment. We adopt a learner/critic approach. We present the Multi-agent Abductive Reasoning System (MARS), a protocol guaranteeing convergence to a situation “sufficiently” satisfying as far as consistency of the system is concerned. Abduction in a full clausal theory has however already a high computational cost in centralized settings, which can become much worse with arbitrary distributions. We thus discuss ways to use knowledge about each agent’s theory language to improve efficiency. We then present some first experimental results to assess the impact of those refinements.