Improve Searching by Reinforcement Learning in Unstructured P2Ps

— Existing searching schemes in unstructured P2Ps can be categorized as either blind or informed. The quality of query results in blind schemes is low. Informed schemes use simple heuristics that lack the theoretical background to support the simulation results. In this paper, we propose to improve searching by reinforcement learning (RL), which has been proven in artificial intelligence to be able to learn the best sequence of actions in order to achieve a certain goal. Our approach, ISRL, aims at locating the best path to desired files at low cost. It explores new paths by forwarding queries to randomly chosen neighbors. It also exploits the paths that have been discovered to reduce the cumulative query cost. Two models of ISRL are proposed: the basic ISRL for finding one desired file, and MPISRL for finding multiple desired files. ISRL outperforms existing searching approaches in unstructured P2Ps by achieving higher query quality with less query traffic. The experimental r...

• Type: Conference
• Year: 2006
• Where: ICDCSW
• Authors: Xiuqi Li, Jie Wu
• Keywords: Blind Schemes, Cumulative Query Cost, Distributed And Parallel Computing, ICDCSW 2006, Unstructured P2ps