Advertisement

Fuzzy Motivations in Behavior Based Agents

  • Tomás V. Arredondo
Part of the Studies in Computational Intelligence book series (SCI, volume 260)

Abstract

In this chapter we describe a fuzzy logic based approach for providing biologically based motivations to be used by agents in evolutionary behavior learning. In this approach, fuzzy logic provides a fitness measure used in the generation of agents with complex behaviors which respond to user expectations of previously specified motivations. Our approach is implemented in behavior based navigation, route planning and action sequence based environment recognition tasks in a Khepera mobile robot simulator. Our fuzzy logic based motivation technique is shown as a simple and powerful method for agents to acquire a diverse set of fit behaviors as well as providing an intuitive user interface framework.

Keywords

Agents fuzzy logic motivations evolutionary mobile robot 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Woolridge, M.: An Introduction to MultiAgent Systems. Wiley, England (2002)Google Scholar
  2. 2.
    Park, H., Kim, E., Kim, H.: Robot Competition Using Gesture Based Interface. In: Moonis, A., Esposito, F. (eds.) Innovations in Applied Artificial Intelligence. LNCS (LNAI), vol. 3353, pp. 131–133. Springer, Berlin (2005)Google Scholar
  3. 3.
    Tasaki, T., Matsumoto, S., Ohba, H., Toda, M., Komatani, K., Ogata, T., Okuno, H.: Distance-Based Dynamic Interaction of Humanoid Robot with Multiple People. In: Moonis, A., Esposito, F. (eds.) Innovations in Applied Artificial Intelligence. LNCS (LNAI), vol. 3353, pp. 111–120. Springer, Berlin (2005)Google Scholar
  4. 4.
    Jensen, B., Tomatis, N., Mayor, L., Drygajlo, A., Siegwart, R.: Robots Meet Humans - Interacion in Public Spaces. IEEE Transactions on Industrial Electronics 52(6), 1530–1546 (2006)CrossRefGoogle Scholar
  5. 5.
    Arkin, R.: Behavior-Based Robotics. MIT Press, Cambridge (1998)Google Scholar
  6. 6.
    Arredondo, T., Freund, W., Muñoz, C., Navarro, N., Quirós, F.: Fuzzy Motivations for Evolutionary Behavior Learning by a Mobile Robot. In: Ali, M., Dapoigny, R. (eds.) IEA/AIE 2006. LNCS (LNAI), vol. 4031, pp. 462–471. Springer, Heidelberg (2006)Google Scholar
  7. 7.
    Pezzulo, G., Calvi, G.: Modulatory Influence of Motivations on a Schema-Based Architecture: A Simulative Study. In: Paiva, A.C.R., Prada, R., Picard, R.W. (eds.) ACII 2007. LNCS, vol. 4738, pp. 374–385. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  8. 8.
    Brooks, R.: A Robust Layered Control System for a Mobile Robot. IEEE Journal of Robotics and Automation RA-2(1), 14–23 (1986)Google Scholar
  9. 9.
    YAKS simulator website, http://freshmeat.net/projects/yaks/
  10. 10.
    Yamada, S.: Evolutionary behavior learning for action-based environment modeling by a mobile robot. Applied Soft Computing 5, 245–257 (2005)CrossRefGoogle Scholar
  11. 11.
    Jang, J., Chuen-Tsai, S., Mitzutani, E.: Neuro-Fuzzy and Soft Computing. Prentice-Hall, Englewood Cliffs (1997)Google Scholar
  12. 12.
    Konolige, K., Meyers, K., Saffiotti, A.: FLAKEY, an Autonomous Mobile Robot. SRI technical document, July 20 (1992)Google Scholar
  13. 13.
    Goodrige, S., Kay, M., Luo, R.: Multi-Layered Fuzzy Behavior Fusion for Reactive Control of an Autonomous Mobile Robot. In: Proceedings of the Sixth IEEE International Conference on Fuzzy Systems, July 1997, pp. 573–578 (1997)Google Scholar
  14. 14.
    Hoffman, F.: Soft computing techniques for the design of mobile robot behaviors. Information Sciences 122, 241–258 (2000)CrossRefGoogle Scholar
  15. 15.
    Al-Khatib, M., Saade, J.: An efficient data-driven fuzzy approach to the motion planning problem of a mobile robot. Fuzzy Sets and Systems 134, 65–82 (2003)zbMATHCrossRefMathSciNetGoogle Scholar
  16. 16.
    Izumi, K., Watanabe, K.: Fuzzy behavior-based control trained by module learning to acquire the adaptive behaviors of mobile robots. Mathematics and Computers in Simulation 51, 233–243 (2000)CrossRefMathSciNetGoogle Scholar
  17. 17.
    Martnez Barber, H., Gmez Skarmeta, A.: A Framework for Defining and Learning Fuzzy Behaviours for Autonomous Mobile Robots. International Journal of Intelligent Systems 17(1), 1–20 (2002)CrossRefGoogle Scholar
  18. 18.
    Zhou, C.: Robot learning with GA-based fuzzy reinforcement learning agents. Information Sciences 145, 45–68 (2002)zbMATHCrossRefGoogle Scholar
  19. 19.
    Seraji, H., Howard, A.: Behavior-Based Robot Navigation on Challenging Terrain: A Fuzzy Logic Approach. IEEE Trans. on Robotics and Automation 18(3), 308–321 (2002)CrossRefGoogle Scholar
  20. 20.
    Huitt, W.: Motivation to learn: An overview. In: Educational Psychology Interactive, Valdosta State University (2001), http://chiron.valdosta.edu/whuitt/col/motivation/motivate.html
  21. 21.
    Jang, J.-S., Sun, C.-T., Sun, M.E.: Neuro-Fuzzy and Soft Computing: a computational approach to learning and machine intelligence. Prentice-Hall, Englewood Cliffs (1997)Google Scholar
  22. 22.
    Teuvo, K.: The self-organizing map. Proceedings of the IEEE 79(9), 1464–1480 (1990)Google Scholar
  23. 23.
    Norman, T.J.: Motivation-based direction of planning attention in agents with goal autonomy, PhD Thesis, Department of Computer Science, University College of London, UK (1997)Google Scholar
  24. 24.
    Coddington, A.M., Luck, M.: A Motivation Based Planning and Execution Framework. International Journal on Artificial Intelligence Tools 13(1), 5–25 (2004)CrossRefGoogle Scholar
  25. 25.
    Karray, F.O., De Silva, C.: Soft Computing and Intelligent Systems Design. Addison Wesley, England (2004)Google Scholar
  26. 26.
    Passino, K.: Biomimicry for Optimization, Control and Automation. Springer, London (2005)zbMATHGoogle Scholar
  27. 27.
    Bylander, T.: The computational complexity of propositional STRIPS planning. Artificial Intelligence 69, 165–204 (1994)zbMATHCrossRefMathSciNetGoogle Scholar
  28. 28.
    Cherniak, C.: Minimal Rationality. MIT Press, Boston (1986)Google Scholar
  29. 29.
    Bratman, M.: Plans and Resource-Bounded Practical Reasoning. Computational Intelligence 4(4), 349–355 (1988)CrossRefGoogle Scholar
  30. 30.
    Rao, A.S., Georgeff, M.P.: BDI Agents From Theory to Practice. In: Proceedings of the First International Conference on Multi Agent Systems (1995)Google Scholar
  31. 31.
    Georgeff, M.P., Ingrand, F.F.: Decision-Making in an Embedded Reasoning System. In: Proceedings of the Eleventh International Joint Conference on Artificial Intelligence (1989)Google Scholar
  32. 32.
    Agile Development at the Wikipedia, http://en.wikipedia.org/wiki/Agile_software_development
  33. 33.
  34. 34.
    Zadeh, L.A.: Fuzzy Logic, Neural Networks, and Soft Computing. Communications of the ACM 37(3), 77–84 (1994)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Tomás V. Arredondo
    • 1
  1. 1.Departamento de ElectrónicaUniversidad Técnica Federico Santa María, Valparaíso, ChileValparaísoChile

Personalised recommendations