Advertisement

Strategy Selection-Based Meta-level Reasoning for Multi-agent Problem-Solving

  • K. Suzanne Barber
  • David C. Han
  • Tse-Hsin Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1957)

Abstract

Researchers have developed various techniques to address MAS problem- solving activities,i.e., agent organization construction, plan generation, task allocation, plan integration, and plan execution. An agent.s respective problem solving and coordination techniques must be properly understood before they can be included into any other software system. ‘Strategies’ describe the techniques by which agents perform their individual decision-making processes and coordinate those processes with other agents. This chapter describes current work in characterizing agent operations, specifically, the representation of strategies in terms of roles and interactions as well as a trade-off evaluation mechanism for deciding which strategy is most appropriate for a given situation. On-line evaluation and selection of strategies will allow agents to tailor their behavior to given environment situations and thus, offer increase flexibility and adaptability of response

Keywords

Task Allocation Coordination Mechanism Plan Execution Plan Integration Negotiation Manager 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Barber, K. S., Liu, T. H., Goel, A., and Ramaswamy, S.: Flexible Reasoning Using Sensible Agent-based Systems: A Case Study in Job Flow Scheduling. Production Planning and Control, 10, 7 (1999) 606–615.CrossRefGoogle Scholar
  2. 2.
    Barber, K. S., Liu, T. H., and Han, D. C. Agent-Oriented Design. In Multi-Agent System Engineering: Proceedings of the 9th European Workshop on Modelling Autonomous Agents in a Multi-Agent World, MAAMAW’99, Valencia, Spain, June 30–July 2, 1999. Lecture Notes in Computer Science: Lecture Notes in Artificial Intelligence, Garijo, F. J. and Boman, M., (eds.). Springer, Berlin, (1999) 28–40.Google Scholar
  3. 3.
    Barber, K. S., McKay, R. M., Martin, C. E., Liu, T. H., Kim, J., Han, D., and Goel, A.: Sensible Agents in Supply Chain Management: An Example Highlighting Procurement and Production Decisions. In Proceedings of 19th ASME Computers and Information in Engineering Conference, Internet-Aided Design, Manufacturing, and Commerce Technical Committee ( Las Vegas, NV, 1999) CIE-9078.Google Scholar
  4. 4.
    Barbuceanu, M. and Fox, M. S.: COOL: A Language for Describing Coordination in Multi-Agent Systems. In Proceedings of First International Conference on Multi-Agent Systems (San Francisco, CA, 1995) AAAI Press/ The MIT Press, 17–24.Google Scholar
  5. 5.
    Bauer, B., Muller, J. P., and Odell, J. Agent UML: A Formalism for Specifying Multiagent Software Systems. This book.Google Scholar
  6. 6.
    Corkill, D. D.: Hierarchical Planning in a Distributed Environment. In Proceedings of Sixth International Joint Conference on Artificial Intelligence (1979) 168–175.Google Scholar
  7. 7.
    Durfee, E. H. and Lesser, V. R.: Using Partial Global Plans to Coordinate Distributed Problem Solvers. In Proceedings of Tenth International Joint Conference on Artificial Intelligence (Milan, Italy, 1987) Morgan Kaufmann, 875–883.Google Scholar
  8. 8.
    Findler, N. V. Contributions to a Computer-Based Theory of Strategies. Springer-Verlag, New York, NY, 1990.CrossRefzbMATHGoogle Scholar
  9. 9.
    Georgeff, M. P.: A Theory of Action for Multi-Agents Planning. In Proceedings of Proceedings of 1984 Conference of the American Association for Artificial Intelligence (1984) 121–125.Google Scholar
  10. 10.
    Grant, R. M. Contemporary Strategy Analysis. Blackwell Publishers Inc, Oxford, UK, 1995.Google Scholar
  11. 11.
    Hayes-Roth, B. A.: Blackboard Architecture for Control. Artificial Intelligence, 26, 3(1985) 251–321.CrossRefGoogle Scholar
  12. 12.
    Ishida, T., Gasser, L., and Yokoo, M.: Organization Self-Design of Distributed Production Systems. IEEE Transactions on Knowledge and Data Engineering, 4, 2 (1992) 123–134.CrossRefGoogle Scholar
  13. 13.
    Jennings, N. R. Coordination Techniques for Distributed Artificial Intelligence. In Foundations of Distributed Artificial Intelligence. Sixth-Generation Computer Technology Series, O’Hare, G. M. P. and Jennings, N. R., (eds.). John Wiley & Sons, Inc., New York, (1996) 187–210.Google Scholar
  14. 14.
    Kendall, E. A. Agent Software Engineering with Role Modelling. This book.Google Scholar
  15. 15.
    Malville, E. and Bourdon, F.: Task Allocation: a Group Self-Design Approach. In Proceedings of Third International Conference on Multi-Agent Systems (Paris, France, 1998) 166–173.Google Scholar
  16. 16.
    Martin, C. E., Macfadzean, R. H., and Barber, K. S.: Supporting Dynamic Adaptive Autonomy for Agent-based Systems. In Proceedings of 1996 Artificial Intelligence and Manufacturing Research Planning Workshop (Albuquerque, NM, 1996) AAAI Press, 112–120.Google Scholar
  17. 17.
    Miles, S., Joy, M., and Luck, M. Designing Agent-Oriented Systems by Analysing Agent Interactions. This book.Google Scholar
  18. 18.
    Odell, J., Parunak, H. V. D., and Bauer, B. Representing Agent Interaction Protocols in UML. In AOSE title to be determined, Ciancarini, P. and Wooldridge, M., (eds.). (2000).Google Scholar
  19. 19.
    Omicini, A. SODA: Societies and Infrastructures in the Analysis and Design of Agent Societies. This book.Google Scholar
  20. 20.
    Smith, R. G.: The Contract Net Protocol: High-level Communication and Control in a Distributed Problem-Solver. IEEE Transactions on Computers, 29, 12 (1980) 1104–1113.CrossRefGoogle Scholar
  21. 21.
    Tambe, M.: Towards Flexible Teamwork. Journal of Artificial Intelligence Research, 7 (1997) 83–124.Google Scholar
  22. 22.
    Vincke, P., Gassner, M., and Roy, B. Multicriteria Decision-aid. John Wiley & Sons., Chichester, 1989.Google Scholar
  23. 23.
    Werner, E. Cooperating Agents: a Unified Theory of Communication and Social Structure. In Distributed Artificial Intelligence II, vol. 2, Gasser, L. and Huhns, M. N., (eds.). Pitman Publishing, London, (1989) 3–36.CrossRefGoogle Scholar
  24. 24.
    Zambonelli, F., Jennings, N. R., and Wooldridge, M. Organisational Abstractions for the Analysis and Design of Multi-Agent Systems. This book.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • K. Suzanne Barber
    • 1
  • David C. Han
    • 1
  • Tse-Hsin Liu
    • 1
  1. 1.Department of Electrical and Computer EngineeringThe University of Texas at Austin

Personalised recommendations