Advertisement

Multi Agent System Based Path Optimization Service for Mobile Robot

  • Huyn Kim
  • TaeChoong Chung
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 124)

Abstract

If a person drives a optimization route recommended by his navigation, considering the person has specific driving habits and propensity and there are many circumstances changes, it is said that the route recommended by a navigation is not optimized.

The prey pursuit problem has being put to use in multi-agents researches with the food chain system using multi agents in the virtual grid space. In this paper, we suggest the limitless space just like reality and the new algorithm to explain reality far enough than the existing grid space.

Keywords

multi agent prey pursuit problem personalization optimized path dynamic environment 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kim, S., Kim, B., Yoon, B.: Multi-agent Coordination Strategy using Reinforcement Learning. In: The Proceedings of the 22nd KIPS Fall Conference, vol. 7-2, pp. 285–288 (2000)Google Scholar
  2. 2.
    Stone, P., Veloso, M.: Multiagent System : A Survey from a Machine Learning, Technical Report CMU-CS-97-193, The University of Carnegie Mellon (December 1997)Google Scholar
  3. 3.
    Haynes, T., Sen, S.: Evolving behavioral strategies in predators and prey. In: Weiband, G., Sen, S. (eds.) Adaptation and Learning in Muiltiagent Systems. Springer, Berlin (1996)Google Scholar
  4. 4.
    Sen, S., Sekaran, M., Hale, J.: Learning to coordinate without sharing information. In: National Conference on Artificial Intelligence, pp. 426–431 (July 1994)Google Scholar
  5. 5.
    Stephens, L.M., Merx, M.B.: The effect og agent control strategy on the performance of a DAI pursuit problem. In: Proceeding of the 1990 Distributed AI Workshop (October 1990)Google Scholar
  6. 6.
    de Jong, E.: Multi-Agent Coordination by Communication of Evaluation. In: Boman, M., Van de Velde, W. (eds.) MAAMAW 1997. LNCS, vol. 1237. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  7. 7.
    Lee, H., Kim, B.: Multiagent Control Strategy using Reinforcement Learning. The KIPS Transactions 10-B 3. 2003.6Google Scholar
  8. 8.
    Levy, R., Rosenschein, J.S.: A game thoretic approach to the pursuit problem. In: Working Papers of the 11th International Workshop on Distributed Atrificial Intelligence (February 1992)Google Scholar
  9. 9.
    Cammarata, S., McArthur, D., Steeb, R.: Strategies of Cooperation in Distributed Problem Solving. In: Proceedings of Eighth International Joint Conference on Artificial Intelligence, Karlsruhe West Germany (August 1993)Google Scholar
  10. 10.
    Stephens, L.M., Merx, M.B.: The effect of agent control strategy on the performance of a DAI pursuit problem. In: Proceeding of the 1990 Distributed AI Workshop (October 1990)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Huyn Kim
    • 1
    • 2
  • TaeChoong Chung
    • 1
    • 2
  1. 1.Department of Computer EngineeringKyunghee UniversitySouth Korea
  2. 2.School of Electronics and InformationKyunghee UniversitySouth Korea

Personalised recommendations