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An Adaptive Agent Society for Environmental Scanning through the Internet

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2132))

Abstract

Business managers need to promptly respond to environmental changes. Environmental scanners are thus important in discovering and monitoring the information of interest (IOI). In this paper, we explore continuous and resource-bounded environmental scanning (CRBES). The scanner continuously scans for new IOI without consuming too much resource (e.g. bandwidths of computer networks and services of information servers). In that case, new IOI may be discovered in a complete and timely manner without making the related networks and servers too exhausted to provide services. We develop a multiagent framework ACES to achieve CRBES. The agents form an adaptive society by adapting their population and specialty to information needs of individual users, resource limitation of environmental scanning, distribution of IOI in the environments, and update behaviors of the IOI. The delivery of ACES to businesses may constantly provide timelier IOI without causing serious problems to the Intranet and the Internet communities.

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References

  1. Andersson M. and Sandholm T.: Time-Quality Tradeoffs in Reallocative Negotiation with Combinatorial Contract Types, Proc. of the 16th National Conference on Artificial Intelligence (1999).

    Google Scholar 

  2. Barbuceanu M.: Coordinating Agents by Role-Based Social Constraints and Conversation Plans, Proc. of AAAI-97 (1997).

    Google Scholar 

  3. Bui H. H., Kieronska D., and Venkatesh S.: Learning Other Agents’ Preferences in Multiagent Negotiation, Proc. of AAAI-96 (1996).

    Google Scholar 

  4. Chen H., Chung Y.-M., Marshall R., and Christopher C. Y.: An Intelligent Personal Spider (Agent) for Dynamic Internet/Intranet Searching, Decision Support Systems 23, 41–58 (1998).

    Article  Google Scholar 

  5. Cuena J. and Ossowski S.: Distributed Models for Decision Support, in Multiagent Systems — A Modern Approach to Distributed Artificial Intelligence, Weiss G. (ed.), The MIT Press (1999).

    Google Scholar 

  6. Decker K. S. and Sycara K.: Intelligent Adaptive Information Agents, Journal of Intelligent Information Systems (1997).

    Google Scholar 

  7. Fazlollahi B., Parikh M. A., and Verma S.: Adaptive Decision Support Systems, Decision Support Systems, Vol. 20, No. 4, 297–315 (1997).

    Article  Google Scholar 

  8. Frolick M. N., Parzinger M. J., Rainer R. K., and Ramarapu N. K.: Using EISs for Environmental Scanning, Information System Management (1997).

    Google Scholar 

  9. Guttman R. H. and Maes P.: Cooperative vs. Competitive Multiagent Negotiations in Retail Electronic Commerce, Proc. of the 2nd International Workshop on Cooperative Information Agents (1998).

    Google Scholar 

  10. Horling B. and Lesser V.: Using Diagnosis to Learn Contextual Coordination Rules, UMass Computer Science Technical Report 99-15 (1999).

    Google Scholar 

  11. Jensen D., Atighetchi M., Vincent R., and Lesser V.: Learning Quantitative Knowledge for Multiagent Coordination, Proc. of AAAI-99 (1999).

    Google Scholar 

  12. Koster M.: Guidelines for Robot Writers, http://info.webcrwaler.com/mak/papers/robots/ guidelines.html. (1993).

  13. Kraus S., Wilkenfeld J., and Zlotkin G.: Multiagent Negotiation under Time Constraints, Artificial Intelligence 75, 297–345 (1995).

    Article  MATH  MathSciNet  Google Scholar 

  14. Lesser V., Horling B., Klassner F., and Raja A.: BIG: A Resource-Bounded Information Gathering Agent, UMass Computer Science Technical Report 1998-03 (1998).

    Google Scholar 

  15. Liu R.-L., Shih M.-J., and Kao Y.-F.: Adaptive Exception Monitoring Agents for Management by Exceptions, to appear in Applied Artificial Intelligence (AAI).

    Google Scholar 

  16. Liu R.-L. and Lin S.-Y.: Adaptive Coordination of Agents for Timely and Resource-Bounded Information Monitoring, Proc. of the 4th International Conference on MultiAgent Systems, Boston, U.S.A, pp. 175–182 (2000).

    Google Scholar 

  17. Liu S.: Business Environment Scanner for Senior Managers: Towards Active Executive Support with Intelligent Agents, Proc. of 31 st Annual Hawaii International Conference on System Sciences, pp. 18–27 (1998).

    Google Scholar 

  18. Openfind: Cyberspace Information Agent 2000 (CIA2000), http://www.openfind. com.tw/About_us/p-2-04.html (2000).

  19. Pilot Software: Pilot Decision Support Suite, http://www.pilotsw.com (1999).

  20. Sandholm T. and Vulkan N.: Bargaining with Deadlines, Proc. of AAAI-99 (1999).

    Google Scholar 

  21. Schwartz R. and Kraus S.: Negotiation on Data Allocation in Multi-Agent Environments, Proc. of AAAI-97 (1997).

    Google Scholar 

  22. Seligman L., Lehner P., Smith K., Elsaesser C., and Mattox D.: Decision-Centric Information Monitoring, Journal of Intelligent Information Systems, 14, 29–50 (2000).

    Article  Google Scholar 

  23. Sugawara T. and Lesser V. R.: Learning to Improve Coordinated Actions in Cooperative Distributed Problem-Solving Environments, Machine Learning (1998).

    Google Scholar 

  24. Volonino L., Waston H. J. and Robinson S.: Using EIS to Respond to Dynamic Business Conditions, Decision Support Systems, Vol. 14, pp. 105–116 (1995).

    Article  Google Scholar 

  25. Willmott S. and Faltings B.: The Benefits of Environment Adaptive Organizations for Agent Coordination and Network Routing Problems, Proc. of the 4th International Conference on MultiAgent Systems, Boston, U.S.A, pp. 333–340 (2000).

    Google Scholar 

  26. Yang C. C., Yen J., and Chen H.: Intelligent Internet Searching Agent Based on Hybrid Simulated Annealing, Decision Support Systems 28, pp. 269–277 (2000).

    Article  Google Scholar 

  27. Zeng D. and Sycara K.: Benefits of Learning in Negotiation, Proc. of AAAI-97 (1997).

    Google Scholar 

  28. Zlotkin G. and Rosenschein J. S.: Cooperation and Conflict Resolution via Negotiation among Autonomous Agents in Noncooperative Domains, IEEE Transactions on System, Man, and Cybernetics, Vol. 21, No. 6 (1991).

    Google Scholar 

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© 2001 Springer-Verlag Berlin Heidelberg

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Liu, RL. (2001). An Adaptive Agent Society for Environmental Scanning through the Internet. In: Yuan, S.T., Yokoo, M. (eds) Intelligent Agents: Specification, Modeling, and Applications. PRIMA 2001. Lecture Notes in Computer Science, vol 2132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44637-0_10

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  • DOI: https://doi.org/10.1007/3-540-44637-0_10

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42434-5

  • Online ISBN: 978-3-540-44637-8

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