Haizheng Zhang
ABSTRACT
The dominant existing routing strategies employed in peer-to-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 queries based on the learned policies and update the estimates based on the new routing policies. Experimental results demonstrate that the learning algorithm improves considerably the routing performance on two test collection sets that have been used in a variety of distributed IR studies.
- S. Abdallah and V. Lesser. Learning the task allocation game. In AAMAS '06: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems, pages 850--857, New York, NY, USA, 2006. ACM Press. Google Scholar
Digital Library
- J. A. Boyan and M. L. Littman. Packet routing in dynamically changing networks: A reinforcement learning approach. In Advances in Neural Information Processing Systems, volume 6, pages 671--678. Morgan Kaufmann Publishers, Inc., 1994.Google ScholarDigital Library
- J. C. French, A. L. Powell, J. P. Callan, C. L. Viles, T. Emmitt, K. J. Prey, and Y. Mou. Comparing the performance of database selection algorithms. In Research and Development in Information Retrieval, pages 238--245, 1999. Google ScholarDigital Library
- B. Horling, R. Mailler, and V. Lesser. Farm: A scalable environment for multi-agent development and evaluation. In Advances in Software Engineering for Multi-Agent Systems, pages 220--237, Berlin, 2004. Springer-Verlag.Google Scholar
- M. Littman and J. Boyan. A distributed reinforcement learning scheme for network routing. In Proceedings of the International Workshop on Applications of Neural Networks to Telecommunications, 1993.Google Scholar
- J. Lu and J. Callan. Federated search of text-based digital libraries in hierarchical peer-to-peer networks. In In ECIR'05, 2005. Google ScholarDigital Library
- J. Lu and J. Callan. User modeling for full-text federated search in peer-to-peer networks. In ACM SIGIR 2006. ACM Press, 2006. Google ScholarDigital Library
- C. D. Manning and H. Schütze. Foundations of Statistical Natural Language Processing. The MIT Press, Cambridge, Massachusetts, 1999. Google ScholarDigital Library
- C. R. Palmer and J. G. Steffan. Generating network topologies that obey power laws. In Proceedings of GLOBECOM '2000, November 2000.Google ScholarCross Ref
- K. Sripanidkulchai, B. Maggs, and H. Zhang. Efficient content location using interest-based locality in peer-topeer systems. In INFOCOM, 2003.Google ScholarCross Ref
- D. Subramanian, P. Druschel, and J. Chen. Ants and reinforcement learning: A case study in routing in dynamic networks. In In Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, pages 832--839, 1997. Google ScholarDigital Library
- J. N. Tao and L. Weaver. A multi-agent, policy gradient approach to network routing. In In Proceedings of the Eighteenth International Conference on Machine Learning, 2001. Google ScholarDigital Library
- H. Zhang, W. B. Croft, B. Levine, and V. Lesser. A multi-agent approach for peer-to-peer information retrieval. In Proceedings of Third International Joint Conference on Autonomous Agents and Multi-Agent Systems, July 2004. Google ScholarDigital Library
- H. Zhang and V. Lesser. Multi-agent based peer-to-peer information retrieval systems with concurrent search sessions. In Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multi-Agent Systems, May 2006. Google ScholarDigital Library
- H. Zhang and V. R. Lesser. A dynamically formed hierarchical agent organization for a distributed content sharing system. In 2004 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2004), 20--24 September 2004, Beijing, China, pages 169--175. IEEE Computer Society, 2004. Google ScholarDigital Library
Index Terms
- A reinforcement learning based distributed search algorithm for hierarchical peer-to-peer information retrieval systems
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