Distributed Consequence Finding: Partition-Based and Cooperative Approaches

When knowledge is physically distributed, information and knowledge of individual agents may not be collected to one agent because they should not be known to others for security and privacy reasons. We thus assume the situation that individual agents cooperate with each other to find useful information from a distributed system to which they belong, without supposing any master or mediate agent who collects all necessary information from the agents. Then we propose two complete algorithms for distributed consequence finding. The first one extends a technique of theorem proving in partition-based knowledge bases. The second one is a more cooperative method than the first one. We compare these two methods and other related approaches in the literature.

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