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Social Control Mechanisms to Coordinate an Unreliable Agent Society

  • Hamid Haidarian Shahri
  • M. Reza Meybodi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2949)

Abstract

In multiagent systems a common problem is how to assign tasks to other agents. It is very desirable to be able to guarantee the error rate of a solution in a multiagent system’s society. In this paper, a novel approach for this problem has been introduced by devising social control mechanisms, analyzing their mathematical models and simulating and comparing them. It is also shown that the open multiagent society is modeled and coordinated in this way and is able to achieve any desired and predetermined threshold of correctness for the final solution, regardless of the performance of selfish and unreliable agents in the society or any stipulation about their honesty. This is extremely critical and problematic in the design of coordinated multiagent societies, today.

Keywords

Error Rate Multiagent System Result Group Task Allocation Agent Society 
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.

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References

  1. 1.
    Agent 2003 Conference on Challenges in Social Simulation, Chicago, IL, USA, October 3-4 (2003)Google Scholar
  2. 2.
    Fourth International Workshop on Engineering Societies in the Agents World, Imperial College, London, UK, October 29-31 (2003)Google Scholar
  3. 3.
  4. 4.
    Lynch, N.A.: Distributed Algorithms. Morgan Kauffman Publishers, Inc., San Francisco (1996)zbMATHGoogle Scholar
  5. 5.
    Mas-Colell, A., Whinston, M., Green, J.R.: Microeconomic Theory. Oxford University Press, Oxford (1995)zbMATHGoogle Scholar
  6. 6.
    Moses, Y., Tenenholtz, M.: On Computational Aspects of Artificial Social Systems. In: Proc. 11th DAI Workshop, Glen Arbor, MI, USA (1992)Google Scholar
  7. 7.
    Nair, R., Ito, T., Tambe, M., Marsella, S.: Task Allocation in the RoboCup Rescue Simulation Domain: A Short Note. In: Birk, A., Coradeschi, S., Tadokoro, S. (eds.) RoboCup 2001. LNCS (LNAI), vol. 2377, p. 751. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  8. 8.
    Overeinder, B., Brazier, F., Marin, O.: Fault Tolerance in Scalable Agent Support Systems: Integrating DARX in the AgentScape Framework. In: 3rd International Symposium on Cluster Computing and the Grid, Tokyo, Japan, May 12-15 (2003)Google Scholar
  9. 9.
    Ramamohanarao, K., Bailey, J.: Transaction Oriented Computational Models for Multi-Agent Systems. In: 13th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2001), Dallas, Texas, USA, November 07-09 (2001)Google Scholar
  10. 10.
    Sandholm, T.W.: Negotiation among Self-Interested Computationally Limited Agents. Ph.D. thesis, University of Massachusetts, Amherst, (1996), http://www.cs.wustl.edu/~sandholm/dissetation.ps
  11. 11.
    Sarmenta, L.F.G.: Volunteer Computing. Ph.D. thesis, Dept. of Electrical Engineering and Computer Science, MIT, Cambridge (December 2000)Google Scholar
  12. 12.
    Shahri, H.H.: Assurance of Solution Correctness in Task Allocation without Stipulation in an Agent Society. In: Proceedings of the Agent 2003 Conference on Challenges in Social Simulation, Chicago, IL, USA (October 2003)Google Scholar
  13. 13.
    Shehory, O., Kraus, S.: Task Allocation via Coalition Formation among Autonomous Agents. In: Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence (IJCAI 1995), Montreal, Quebec, Canada (August 1995)Google Scholar
  14. 14.
    Sparkman, C.H., De Loach, S.A., Self, A.L.: Automated Derivation of Complex Agent Architectures from Analysis Specifications. In: Wooldridge, M.J., Weiß, G., Ciancarini, P. (eds.) AOSE 2001. LNCS, vol. 2222, p. 278. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  15. 15.
    Wallace, S.: Validating Agent Behavior. In: 23rd Soar Workshop, Ann Arbor, Michigan, USA, June 23-27 (2003)Google Scholar
  16. 16.
    Winoto, P.: A Multi-Agent Based Simulation of the Market for Offenses. In: AAAI 2002 Workshop on Multi-Agent Modeling and Simulation of Economic Systems (MAMSES 2002), Edmonton, Canada, July 29 (2002)Google Scholar
  17. 17.
    Zuev, Y.A.: The Estimation of Efficiency of Voting Procedures. Theory of Probability and its Applications 42(1) (March 1997), http://www.siam.org/journals/tvp/42-1/97594.html

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Hamid Haidarian Shahri
    • 1
  • M. Reza Meybodi
    • 1
  1. 1.Faculty of Computer Engineering and Information TechnologyAmirkabir University of Technology (Tehran Polytechnic)TehranIran

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