Skip to main content
Log in

A multiagent dynamic interaction testbed: Theoretic framework, system architecture and experimentation

  • Published:
Journal of Computer Science and Technology Aims and scope Submit manuscript

Abstract

Recent research on Distributed Artificial Intelligence (DAI) has focused upon agents’ interaction in Multiagent Systems. This paper presents a text understanding oriented multiagent dynamic interaction testbed (TUMIT): the theoretic framework based upon game theory, the free-market-like system architecture, and experimentation on TUMIT. Unlike other DAI testbeds, TUMIT views different text understanding (TU) methods as different “computational resources”, and makes agents choose different TU paths and computational resources according to the resource information on the bulletins in their hostcomputer. Therefore, in TUMIT, task allocation is wholly distributed. This makes TUMIT work like a “free market”. In such a system, agents’ choices and resource load may oscillate. It is shown theoretically and experimentally that if agents use multi-level of “history information”, their behavior will tend to converge to a Nash equilibrium situation; and that if agents use “recall-forget” strategy on “history information”, the convergence can be accelerated and the agents can acclimate themselves to changed environment. Compared with other DAI testbeds, TUMIT is more distributed, and the agents in TUMIT are more adaptive to the dynamic environment.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Lesser V R, Corkill D. Functionally accurate, cooperative distributed systems.IEEE Trans. Systems, Man, and Cybernetics, 1981, 11(1): 81–96.

    Article  Google Scholar 

  2. Durfee E H, Rosenschein J S. Distributed problem solving and multi-agent systems: Comparisons ad eamples. InProc. 13th International Distributed Artificial Intelligence Workshop, Seattle, WA, USA, July, 1994, pp.94–104.

  3. Gmytrasiewicz P J, Durfee E H. A decision theoretic approach to coordinating multiagent interactions. InProc. the 12th International Joint Conference on Artificial Intelligence, Sydney, Australia, August, 1991, pp.62–68.

  4. Zlotkin G, Rosenschein J S. Cooperation and conflict resolution via negotiation among autonomous agents in noncooperative domains.IEEE Trans. Systems, Man, and Cybernetics, 1991, 21(6): 1317–1324.

    Article  MATH  Google Scholar 

  5. Huberman B A, Hogg T. The behavior of computational ecologies.The Ecology of Computation, Huberman B A (ed.), 1988.

  6. Wang Xuejun, Shi Chunyi. Collaboration planning and conflict resolution among multi autonomous robots. InProc. 1994 International Conference On Intelligent Robots, (IROS’94), Munich, Germany, September, 1994, pp.1355–1359.

  7. Wang Xuejun, Shi Chunyi. Learning to coordinate with incomplete information in multi-robot system. InProc. 1995 International Conference On Intelligent Manufacture, Wuhan, China, June, 1995.

  8. Wang Xuejun, Shi Chunyi, Hu Peng. The dynamics of an open DAI system.Chinese Journal of Software, 1995, 6(10).

  9. David R, Smith R. Negotiation as a Metaphor for distributed problem solving.Artificial Intelligence, 1983, 20.

  10. Nash J F. Non-cooperative games.Annals of Maths, 1991, 54: 286–295.

    Article  MathSciNet  Google Scholar 

  11. Shi Chunyi, Wang Xuejunet al. TUMIT—A text understanding oriented multiagent interaction testbed. Technical Report, Tsinghua University, 1995.

  12. Russell S J, Subramanian D, Parr R. Provably bounded optimal agents. InProc. 13th International Joint Conference on Artificial Intelligence, Chambery, France, August, 1993, pp.338–344.

  13. Alterman R, Zito-Wolf R. Agents, habitats, and routine behavior. InProc. 13th International Joint Conference on Artificial Intelligence, Chambery, France, August, 1993, pp.305–310.

  14. Grosz B, Kraus S. Collaborative plans for group activities. InProc. 13th International Joint Conference on Artificial Intelligence, Chambery, France, August, 1993, pp.367–373.

  15. Sycara Ket al. Distributed constrained heuristic search.IEEE Trans. Systems, Man, and Cybernetics, 1991, 21(6): 1446–1461.

    Article  Google Scholar 

  16. Wang Xuejun, Shi Chunyiet al. A text understanding oriented multiagent dynamic interaction testbed: Theoretic framework, system architecture and experimentation. InProc. 1995 IEEE International Conference on System, Man and Cybernetics, Vancouver, Canada, Oct., 1995, pp.800–805.

  17. DAI Research Group of Tsinghua University. Text Understanding Oriented Multiagent System —MAS/TH3. Technical Report, Tsinghua University, April, 1994.

  18. Rosenschein J S. Rational interaction: Cooperation among intelligent agents. Ph.D. thesis, Stanford University, 1986.

  19. Thomas L C. Games, Theory and Application. Chichester, 1984.

Download references

Author information

Authors and Affiliations

Authors

Additional information

Wang Xuejun is a Ph.D. candidate in Tsinghua University. His research interests are distributed artificial intelligent.

Shi Chunyi is a Professor in Tsinghua University. His research interests are artificial intelligent application basis and knowledge engineering.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wang, X., Shi, C. A multiagent dynamic interaction testbed: Theoretic framework, system architecture and experimentation. J. of Comput. Sci. & Technol. 12, 121–132 (1997). https://doi.org/10.1007/BF02951331

Download citation

  • Received:

  • Revised:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF02951331

Keywords

Navigation