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An Optimized Solution for Multi-agent Coordination Using Integrated GA-Fuzzy Approach in Rescue Simulation Environment

  • Mohammad Goodarzi
  • Ashkan Radmand
  • Eslam Nazemi
Part of the Studies in Computational Intelligence book series (SCI, volume 325)

Abstract

Agents’ coordination, communication and information sharing have been always open problems in multi-agent research fields. In complex rescue simulation environment, each agent observes a large amount of data which exponentially increases through the time while the capacity of messages in which agents’ information is shared with others and also the time needed to process the data is limited. Apparently, having a suitable coordination strategy and data sharing system will lead to better overall performance. Therefore, agents should select and spread out the most useful data among their observed information in order to achieve better coordination. In this paper, we propose an unique method based on integration of Genetic Algorithms and Fuzzy Logic theory to decide which part of data is more important to share in different situations. We also advise a new iterative method in order to obtain admissible experimental results in rescue simulation environment which is a good measurement for our research.

Keywords

Membership Function MultiAgent System Fuzzy Logic System Message Type Training Situation 
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.
    Al jaddan, O., Rajamani, L.: Improved Selection Operator for GA. Journal of Theoretical and Applied Information Technology, 269–277 (2005)Google Scholar
  2. 2.
    Banzhaf, W., Nordin, P., Keller, R., Francone, F.: Genetic Programming - An Introduction. Morgan Kaufmann, San Francisco (1998)zbMATHGoogle Scholar
  3. 3.
    Dubois, D., Prade, H.: Fuzzy Sets and Systems: Theory and Applications. Academic Press, New York (1980)zbMATHGoogle Scholar
  4. 4.
    Goldberf, David, E.: Genetic Algorithms in Search, Optimization and Machine Learning. Kluwer Academic Publishers, Boston (1989)Google Scholar
  5. 5.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)Google Scholar
  6. 6.
    Kruse, R., Gebhardt, J., Kalwonn, F.: Foundations of Fuzzy Systems. John Wiley & Sons, New York (1994)Google Scholar
  7. 7.
    Morimoto, T.: How to Develop a RoboCup Rescue Agent. Rescue Technical Committee (2002)Google Scholar
  8. 8.
    Radmand, A., Nazemi, E., Goodarzi, M.: Integrated Genetic Algorithmic and Fuzzy Logic Approach for Decision Making of Police Force Agents in Rescue Simulation Environment. In: Baltes, J., Lagoudakis, M.G., Naruse, T., Ghidary, S.S. (eds.) RoboCup 2009: Robot Soccer World Cup XIII. LNCS, vol. 5949, pp. 288–295. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Siddhartha, H., Sarika, R., Karlapalem, K.: Score Vector: A New Evaluation Scheme for RoboCup Rescue Simulation Competition 2009, Rescue Technical Committee (2009)Google Scholar
  10. 10.
    Takagi, T., Sugeno, M.: Fuzzy Identification of Systems and Its Applications to Modeling and Control. IEEE Transactions on systems, man and cybernetics SMC-15(1) (January/February 1985)Google Scholar
  11. 11.
    Xu, Y., Lewis, M., Sycara, K., Scerri, P.: Information Sharing in Large Scale Teams. In: AAMAS 2004 Workshop on Challenges in Coordination of Large Scale MultiAgent Systems (2004)Google Scholar
  12. 12.
    Zimmermann, H.-J.: Fuzzy Set Theory and its Applications, 2nd edn. Kluwer Academic Publishers, Boston (1991)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Mohammad Goodarzi
    • 1
  • Ashkan Radmand
    • 1
  • Eslam Nazemi
    • 1
  1. 1.Faculty of Electrical and Computer EngineeringShahid Beheshti UniversityIran

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