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Coordinated Collaboration of Multiagent Systems Based on Genetic Algorithms

  • Keon Myung Lee
  • Jee-Hyong Lee
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2891)

Abstract

This paper is concerned with coordinated collaboration of multiagent systems in which there exist multiple agents which have their own set of skills to perform some tasks, multiple external resources which can be either used exclusively by an agent or shared by the specified number of agents at a time, and a set of tasks which consist of a collection of subtasks each of which can be carried out by an agent. Even though a subtask can be carried out by several agents, its processing cost may be different depending on which agent performs it. To process tasks, some coordination work is required such as allocating their constituent subtasks among competent agents and scheduling the allocated subtasks to determine their processing order at each agent. This paper proposes a genetic algorithm-based method to coordinate the agents to process tasks in the considered multiagent environments. It also presents some experiment results for the proposed method and discusses the pros and cons of the proposed method.

Keywords

Genetic Algorithm Candidate Solution Multiagent System Task Schedule Task Allocation 
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. [FER]
    Ferber, J.: Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence. Addison-Wesley Longman, Amsterdam (1999)Google Scholar
  2. [WEI]
    Weiss, G.: Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence (eds.). The MIT Press, Cambridge (1999)Google Scholar
  3. [CAR]
    Cardon, A., Galinho, T., Vacher, J.-P.: Genetic algorithms using multiobjectives in a multi-agent system. Robotics and Autonomous Systems 33, 179–190 (2000)CrossRefGoogle Scholar
  4. [VAZ]
    Vazquez, M., Whitley, L.D.: A Comparison of Genetic Algorithms for the Dynamic Job Shop Scheduling Problem. In: GECCO-2000, pp. 1011–1018 (2000)Google Scholar
  5. [LAB]
    Laborie, P.: Algorithms for propagating resource constraints in AI planning and scheduling: Existing approaches and new results. Artificial Intelligence 143, 151–188 (2003)MATHCrossRefMathSciNetGoogle Scholar
  6. [FAN]
    Fang, H.-L.: Genetic Algorithms in Timetabling and Scheduling. Ph.D. Dissertation, Univ. of Edingburgh (1994)Google Scholar
  7. [BLA]
    Blazewicz, J., Ecker, K., Schmit, G., Weglarz, J.: Scheduling in Computer and Manufacturing Systems. Springer, Heidelberg (1993)MATHGoogle Scholar
  8. [LEE]
    Lee, K.-M., Yamakawa, T., Lee, K.M.: Genetic algorithm approaches to job shop scheduling problems: An Overview. Int. Journal of Knowledge-based Intelligent Engineering Systems 4(2), 72–85 (2000)Google Scholar
  9. [DEI]
    Deitel, H.M.: Operating Systems. Addison-Wesley, Reading (1990)Google Scholar
  10. [MIT]
    Mitchell, M.: An Introduction to Genetic Algorithms. The MIT Press, Cambridge (1998)MATHGoogle Scholar
  11. [HUR]
    Hurink, J., Jurisch, B., Tole, M.: Tabu search for the job shop scheduling problem with multi-purpose machines. In: Operations Research Specktrum, vol. 15, pp. 205–215 (1994)Google Scholar
  12. [RUS]
    Russell, S.J., Norvig, P.: Artificial Intelligence: A Mordern Approach. Prentice-Hall, Englewood Cliffs (1995)Google Scholar
  13. [WIL]
    Wilkins, D.E., Myers, K.L.: A multiagent planning architecture. In: AIPS 1998, pp. 154–162 (1998)Google Scholar
  14. [WOL]
    Wolverton, M.J., desJardins, M.: Controling communication in distributed planning using irrelevance reasoning. In: AAAI 1998 (1998)Google Scholar
  15. [KOB]
    Kobayashi, S., Ono, I., Yamamura, M.: An Efficient Genetic Algorithm for Job Shop Scheduling Problems. In: Proc. of the 6-th Int. Conf. on Genetic Algorithms, pp. 506–511 (1995)Google Scholar
  16. [DES]
    desJardins, M.E., Durfee, E.H., Le Ortiz Jr., C., Wolverton, M.J.: A Survey of Resarch in Distributed, Continual Planning. AI Magazine (Winter 2002)Google Scholar
  17. [COR]
    Corkill, D.D.: Hierarchical planning in a distributed environment. In: IJCAI 1979 (1979)Google Scholar
  18. [DUR]
    Durfee, E.H., Lesser, V.R.: Partial global planning: A coordinationframework for distributed hypothesis formation. IEEE Trans. on Systems, Man, and Cybernetics KDE-1, 63–83 (1991)Google Scholar
  19. [SCH]
    Schoppers, M.J.: Universal plans for reactive robots in unpredictable environments. In: IJCAI 1987, pp. 1039–1046 (1987)Google Scholar
  20. [DEC]
    Decker, K.S., Lesser, V.R.: Designing a family of coordination algorithms. In: ICMAS 1995 (1995)Google Scholar
  21. [KNO]
    Knoblock, C.A.: Planning, Executing, Sensing, and Replanning for Information Gathering. In: IJCAI 1995 (1995)Google Scholar
  22. [BAR]
    Grosz, B.J., Kraus, S.: Collaborative plans for complex group action. Artificial Intelligence 86(1), 269–357 (1996)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Keon Myung Lee
    • 1
  • Jee-Hyong Lee
    • 2
  1. 1.School of Electric and Computer EngineeringChungbuk National University, and Advanced Information Technology Research Center(AITrc)Korea
  2. 2.School of Information and Communication EngineeringSungKyunKwan UniversityKorea

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