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This book frames a peer-to-peer information retrieval problem as a multi-agent framework and attacks it from an organizational perspective by exploring various adaptive, self-organizing topological organizations, designing appropriate coordination strategies, and exploiting learning techniques to create more accurate routing policy for large-scale agent organizations. In addition, a reinforcement-learning based approach is developed in this thesis to take advantage of the run-time characteristics of P2P IR systems, including environmental parameters, bandwidth usage, and historical information about past search sessions. In the learning process, agents refine their content routing policies by constructing relatively accurate routing tables based on a Q-learning algorithm. Experimental results show that this learning algorithm considerably improves the performance of distributed search sessions in P2P IR systems. The book is addressed to researchers and practitioners in information retrieval and search engine, content-based routing areas.
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This book frames a peer-to-peer information retrieval problem as a multi-agent framework and attacks it from an organizational perspective by exploring various adaptive, self-organizing topological organizations, designing appropriate coordination strategies, and exploiting learning techniques to create more accurate routing policy for large-scale agent organizations. In addition, a reinforcement-learning based approach is developed in this thesis to take advantage of the run-time characteristics of P2P IR systems, including environmental parameters, bandwidth usage, and historical information about past search sessions. In the learning process, agents refine their content routing policies by constructing relatively accurate routing tables based on a Q-learning algorithm. Experimental results show that this learning algorithm considerably improves the performance of distributed search sessions in P2P IR systems. The book is addressed to researchers and practitioners in information retrieval and search engine, content-based routing areas.
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Toward Peer-to-Peer Based Semantic Search Engines: An Organizational Approach
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
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Toward Peer-to-Peer Based Semantic Search Engines
Verlag:
VDM Verlag Dr. Müller|VDM Verlag Dr. Müller e.K., 2008
ISBN 10: 3639084799
ISBN 13: 9783639084795
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Kartoniert / Broschiert
Anbieter: moluna, Greven, Deutschland
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Kartoniert / Broschiert. Zustand: New. This book frames a peer-to-peer information retrievalproblem as a multi-agent framework and attacks itfrom an organizational perspective by exploringvarious adaptive, self-organizing topologicalorganizations, designing appropriatecoordination strategies, an. Artikel-Nr. 4955989
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Toward Peer-to-Peer Based Semantic Search Engines : An Organizational Approach
Verlag:
VDM Verlag Dr. Müller, VDM Verlag Dr. Müller E.K., 2008
ISBN 10: 3639084799
ISBN 13: 9783639084795
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Taschenbuch
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
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Taschenbuch. Zustand: Neu. Neuware - This book frames a peer-to-peer information retrievalproblem as a multi-agent framework and attacks itfrom an organizational perspective by exploringvarious adaptive, self-organizing topologicalorganizations, designing appropriatecoordination strategies, and exploiting learningtechniques to create more accurate routing policy forlarge-scale agent organizations. In addition, areinforcement-learning based approach is developed inthis thesis to take advantage of the run-timecharacteristics of P2P IR systems, includingenvironmental parameters, bandwidth usage, andhistorical information about past search sessions. Inthe learning process, agents refine their contentrouting policies by constructing relatively accuraterouting tables based on a Q-learning algorithm.Experimental results show that this learningalgorithm considerably improves the performance ofdistributed search sessions in P2P IR systems.The book is addressed to researchers andpractitioners in information retrieval and searchengine, content-based routing areas. Artikel-Nr. 9783639084795
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