Browse > Article
http://dx.doi.org/10.5391/JKIIS.2004.14.2.156

A Coordinated Collaboration Method of Multiagent Systems based on Genetic Algorithms  

Sohn, Bong-Ki (충북대학교 전기전자컴퓨터공학부, 컴퓨터정보통신연구소, 첨단정보기술연구센터(AITrc))
Lee, Keon-Myung (충북대학교 전기전자컴퓨터공학부, 컴퓨터정보통신연구소, 첨단정보기술연구센터(AITrc))
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.14, no.2, 2004 , pp. 156-163 More about this Journal
Abstract
This paper is concerned with coordinated collaboration of multiagent system 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 consists 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 shows that the proposed method is a useful coordination collaboration method of multiagent system.
Keywords
멀티에이전트시스템;조정 협동;유전알고리즘;작업 할당;작업 스케줄링;
Citations & Related Records
연도 인용수 순위
  • Reference
1 M. Vazquez, L. D. Whitley, A Comparison of Genetic Algorithms for the Dynamic Job Shop Scheduling Problem, GECCO-2000, pp. 1011-1018, 2000.
2 P. Laborie, Algorithims for propagating resource constraints in AI planning and scheduling: Existing approaches and new results, Artificial Intelligence, Vol. 143, pp.151-188, 2003.   DOI   ScienceOn
3 J. Blazewicz, K. Ecker, G. Schmit, J. Weglarz, Scheduling in Computer and Manufacturing Systems, Springer-Verlag, 1993.
4 S. Kobayshi, I. Ono, M. Yamamura, An Efficient Genetic Algorithm for Job Shop Scheduling Problems, AI Magazine, Winter, 2002.
5 K.S. Decker, V.R. Lesser, Designing a family of coordination algorithms, In ICMAS-95, (1995).
6 H.-L. Fang, Genetic Algorithms in Timetabling and Scheduling, Ph.D. Dissertation, Univ. of Edingburgh, 1994.
7 J. Ferber, Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence, Addison Wesley Longman, 1999.
8 E.H. Durfee, V.R. Lesser, Partial global planning: A coordination framework for distributed hypothesis formation, IEEE Trans. on Systems, Man, and Cybernetics, KDE-1, (1991) 63-83.   DOI   ScienceOn
9 M. Mitchell, An Introduction to Genetic Algorithms, The MIT Press, 1998.
10 J. Hurink, B. Jurisch, M. Tole, Tabu search for the job shop scheduling problems with multi- purpose machines, In Operations Research Spectrum, Vol. 15, pp.205-215, 1994.   DOI
11 K. -M. Lee, T. Yamakawa, K. M. Lee, “Genetic algorithm approaches to job shop scheduling problems: An overview”, Int. Journal of Knowledge- based Intelligent Engineering Systems, NO. 2, pp.72-85, 2000.
12 G. Weiss, Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence(eds.), The MIT Press, 1999.
13 M.E. desJardins, E.H. Durfee, C. Le Ortiz, Jr., M.J. Wolverton, A survey of Research in Distributed, Continual Planning, AI Magazine, Winter (2002).
14 S. J. Russell, P. Norvig, Artificial Intelligence:A Morden Approach, Prentice Hall, 1995.
15 H. M. Deitel, Operating Systems, Addison Wesley. 1990.