A reinforcement learning based distributed search algorithm for hierarchical peer-to-peer information retrieval systems

The dominant existing routing strategies employed in peerto-peer(P2P) based information retrieval(IR) systems are similarity-based approaches. In these approaches, agents depend on the content similarity between incoming queries and their direct neighboring agents to direct the distributed search sessions. However, such a heuristic is myopic in that the neighboring agents may not be connected to more relevant agents. In this paper, an online reinforcement-learning based approach is developed to take advantage of the dynamic run-time characteristics of P2P IR systems as represented by information about past search sessions. Specifically, agents maintain estimates on the downstream agents' abilities to provide relevant documents for incoming queries. These estimates are updated gradually by learning from the feedback information returned from previous search sessions. Based on this information, the agents derive corresponding routing policies. Thereafter, these agents route the que...

• Type: Conference
• Year: 2007
• Where: ATAL
• Authors: Haizheng Zhang, Victor R. Lesser
• Keywords: ATAL 2007, Direct Neighboring Agents, Intelligent Agents, Routing Policies, Search Sessions