収入単調性を満たすオークションメカニズムの特性及びその架空名義操作不可能性との関係

概要

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This paper proposes a novel Peer-to-Peer Information Retrieval (P2PIR) method using user feedback and query-destination-learning. The method uses positive feedback information effectively for getting documents relevant to a query by giving higher score to them. The method also utilize negative feedback information actively so that other agents can filter it out with itself. Using query-destination-learning, the method can not only accumulate relevant information from all the member agents in a community, but also reduce communication loads by caching queries and their sender-responder agent addresses in the community. Experiments were carried out on multiple communities constructed with multi-agent framework Kodama. The experimental results illustrated that the proposed method effectively increased retrieval accuracy.