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HAWK (Ha rvesting The W idely Distributed K nowledge) …Wolfgang Theilmann, Kurt Rothermel

HAWK (Ha rvesting The W idely Distributed K nowledge) …Wolfgang Theilmann, Kurt Rothermel. Sidney D’Mello Control of Autonomous Agents October 29, 2002. Project Goals. Distributed solution for information retrieval. Group document metadata according to content.

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HAWK (Ha rvesting The W idely Distributed K nowledge) …Wolfgang Theilmann, Kurt Rothermel

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  1. HAWK (Harvesting The Widely Distributed Knowledge)…Wolfgang Theilmann, Kurt Rothermel Sidney D’Mello Control of Autonomous Agents October 29, 2002

  2. Project Goals • Distributed solution for information retrieval. • Group document metadata according to content. • Employ different strategies for different domains.

  3. Environmental Problems • Size of the World Wide Web • Dynamic content. • No central authority. • Heterogeneity.

  4. System Multiagent System: • Domain Experts: Knowledge Acquisition • Mobile Agents: Knowledge Investigation

  5. Overview-Domain Experts • Environment: World Wide Web. • Sensors: User query. • Goals: Efficient, high quality information retrieval. Proactivity. • Actions: Satisfy goals (discussed later).

  6. Learning • Start with a domain but no documents. • Uses existing search engines. • Examines documents by mobile filter agents. • Build a knowledge base about its domain. • Eventually directly answers user queries.

  7. Actions (Domain Expert) • Accept user query. • Activate knowledge gatherer. • Internally query existing document knowledge. • Activate mobile agents. • Respond to user.

  8. Action selection algorithm 1. Accept the user’s query. 2. If knowledge exists in knowledge base: a. Update knowledge by invoking knowledge gatherer. b. Reply to user query and exit. 3. Else: a. Forward result URLs to mobile agents. b. Update knowledge base. c. Reply to user query and exit.

  9. Processing a simple query

  10. Architecture of a domain expert

  11. Overview: Mobile Filter Agents • Environment: World Wide Web. • Sensors: List of unexamined or updated links. • Goals: Examine novel or updated documents. • Actions: Transfer code and data to remote locations. Start remote execution. Refuse remote execution.

  12. Action Selection Algorithm: 1. Accept list of links and remote location. 2. Transfer code and data to remote location. 3. If can process links: a. Accept new links. 4. Else a. Reject new links. 5. Report results to agent-coordinator.

  13. Life Cycle: Mobile Filter Agent • Distribution • Analysation • IntermediateResults • Integration

  14. Example Scenario

  15. Proactivity • Examine the environment of an interesting document. • Process relevant documents for further hints. • Query external knowledge bases for new information.

  16. Generic Platform • Flexibility Issues • Filter Agent: a. Format Abstraction b. Domain Tester • Returns a fuzzy value.

  17. Facets • Examples: keyword, author, etc. • Mechanism to compute the instances of a facet. • New facets can be added. • User queries can be extended. • Increases flexibility.

  18. Access to domain experts • Domain experts register themselves. • Create a hierarchical structure. • Users select an expert. • Advanced queries possible by tuning facets.

  19. Example Application Areas • Expert for the publication domain. • Expert for downloadable software.

  20. Conclusions • Prefilter documents on the Web. • More precise queries can be created easily. • Approach is flexible. • Proactive search. • Higher recall than traditional search engines.

  21. References • Domain Experts for Information Retrieval in the World Wide Web Wolfgang Thielmann, Kurt Rothermel • Maintaining Specialized Search Engines through Mobile Filter Agents. Wolfgang Thielmann, Kurt Rothermel The University of Stuttgart, Germany

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