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Textual content provides a clean point of view on e-government thought and the problems concerning imposing e-government tasks. For lecturers and practitioners who're searching for ideas to e-government tasks being applied by means of their firms. Hardcover, softcover to be had. DLC: net in public administration--United States.
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Thien Huynh in programming efforts on several parts of this research project. REFERENCES Bigus, J. P. & Bigus, J. (1998). Constructing Intelligent Agents with Java — A Programmer’s Guide to Smarter Applications. Hoboken, NJ: Wiley InterScience. , & Zambonelli, F. (2000, February). Mobile-agent coordination models for Internet applications. IEEE Spectrum. , & Yang, C. (1997). Intelligent spider for Internet searching. In Proceedings of the 30th Hawaii International Conference on System Sciences (vol.
Some agents can be trained to learn through examples in order to improve the performance of the tasks they are given (see Watson, 1997; Bigus & Bigus, 1998). There are also several ways that agents can be trained to better understand user preferences by using computational intelligence techniques, such as using evolutionary computing systems, neural networks, adaptive fuzzy logic and expert systems, etc. The combination of search and retrieval engines, the agent, the user preference, and the information retrieval algorithm can provide the users with the confidence and trust they require in agents.
2002). The problem with petrabytes. The Information Architect. au Jensen, J. (2002). Using an intelligent agent to enhance search engine performance. htm Jentzsch, R. & Gobbin, R. (2002). An e-commerce communicative multi-agent agent model. Proceedings of the IRMA 2002 Conference, Seattle, Washington (May 19-22). Copyright © 2004, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. Web Data Mining and Information Retrieval 29 Lawrence, S.