Linear resolution for consequence finding
Artificial Intelligence
Representing agent interaction protocols in UML
First international workshop, AOSE 2000 on Agent-oriented software engineering
Database Updates through Abduction
VLDB '90 Proceedings of the 16th International Conference on Very Large Data Bases
Hypotheses refinement under topological communication constraints
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
DARE: a system for distributed abductive reasoning
Autonomous Agents and Multi-Agent Systems
Distributed reasoning in a peer-to-peer setting: application to the semantic web
Journal of Artificial Intelligence Research
Partition-based logical reasoning for first-order and propositional theories
Artificial Intelligence - Special volume on reformulation
Evaluating abductive hypotheses using an EM algorithm on BDDs
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Abduction of distributed theories through local interactions
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
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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.