出版社：The Japanese Society for Artificial Intelligence
摘要：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 utilizes 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 both single and multiple communities constructed with multi-agent framework Kodama . The experimental results illustrated that the proposed method effectively increased retrieval accuracy.
关键词：Multi-Agents ; Community ; Information Retrieval ; Peer-to-Peer ; User Feedback ; Query Destination Address Learning
