Advertisement

Retrieving and Reusing Game Plays for Robot Soccer

  • Raquel Ros
  • Manuela Veloso
  • Ramon López de Màntaras
  • Carles Sierra
  • Josep Lluís Arcos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4106)

Abstract

The problem of defining robot behaviors to completely address a large and complex set of situations is very challenging. We present an approach for robot’s action selection in the robot soccer domain using Case-Based Reasoning techniques. A case represents a snapshot of the game at time t and the actions the robot should perform in that situation. We basically focus our work on the retrieval and reuse steps of the system, presenting the similarity functions and a planning process to adapt the current problem to a case. We present first results of the performance of the system under simulation and the analysis of the parameters used in the approach.

Keywords

Game Play Aggregation Function Robot Behavior Robot Soccer Robotic Soccer 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aamodt, A., Plaza, E.: Case-based reasoning: Foundational issues, methodological variations, and system approaches. Artificial Intelligence Communications 7(1), 39–59 (1994)Google Scholar
  2. 2.
    RoboCup Technical Committee. Sony Four Legged Robot Football League Rule Book (December 2004)Google Scholar
  3. 3.
    Gabel, T., Veloso, M.: Selecting heterogeneous team players by case-based reasoning: A case study in robotic soccer simulation. Technical report CMU-CS-01-165, Carnegie Mellon University (2001)Google Scholar
  4. 4.
    Haigh, K., Veloso, M.: Route planning by analogy. In: International Conference on Case-Based Reasoning, October 1995, pp. 169–180 (1995)Google Scholar
  5. 5.
    Karol, A., Nebel, B., Stanton, C., Williams, M.: Case Based Game Play in the RoboCup Four-Legged League Part I The Theoretical Model. In: Polani, D., Browning, B., Bonarini, A., Yoshida, K. (eds.) RoboCup 2003. LNCS, vol. 3020, pp. 739–747. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Kruusmaa, M.: Global navigation in dynamic environments using case-based reasoning. Auton. Robots 14(1), 71–91 (2003)MATHCrossRefGoogle Scholar
  7. 7.
    Lattner, A., Miene, A., Visser, U., Herzog, O.: Sequential Pattern Mining for Situation and Behavior Prediction in Simulated Robotic Soccer. In: 9th RoboCup International Symposium (2005)Google Scholar
  8. 8.
    Likhachev, M., Arkin, R.: Spatio-temporal case-based reasoning for behavioral selection. In: ICRA, pp. 1627–1634 (2001)Google Scholar
  9. 9.
    Marling, C., Tomko, M., Gillen, M., Alexander, D., Chelberg, D.: Case-based reasoning for planning and world modeling in the robocup small size league. In: IJCAI Workshop on Issues in Designing Physical Agents for Dynamic Real-Time Environments (2003)Google Scholar
  10. 10.
    Ram, A., Santamaria, J.C.: Continuous case-based reasoning. Artificial Intelligence 90(1-2), 25–77 (1997)MATHCrossRefGoogle Scholar
  11. 11.
    Riedmiller, M., Merke, A., Meier, D., Hoffmann, A., Sinner, A., Thate, O., Ehrmann, R.: Karlsruhe brainstormers — A reinforcement learning approach to robotic soccer. In: Stone, P., Balch, T., Kraetzschmar, G.K. (eds.) RoboCup 2000. LNCS (LNAI), vol. 2019. Springer, Heidelberg (2001)Google Scholar
  12. 12.
    Ros, R., de Màntaras, R.L., Sierra, C., Arcos, J.L.: A CBR system for autonomous robot navigation. In: Proceedings of CCIA 2005, vol. 131 (2005)Google Scholar
  13. 13.
    Sarje, A., Chawre, A., Nair, S.: Reinforcement Learning of Player Agents in RoboCup Soccer Simulation. In: Fourth International Conference on Hybrid Intelligent Systems, pp. 480–481 (2004)Google Scholar
  14. 14.
    Wendler, J., Brüggert, S., Burkhard, H., Myritz, H.: Fault-tolerant self localization by case-based reasoning. In: Birk, A., Coradeschi, S., Tadokoro, S. (eds.) RoboCup 2001. LNCS (LNAI), vol. 2377. Springer, Heidelberg (2002)Google Scholar
  15. 15.
    Wendler, J., Lenz, M.: CBR for Dynamic Situation Assessment in an Agent-Oriented Setting. In: Proc. AAAI 1998 Workshop on CBR Integrations (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Raquel Ros
    • 1
  • Manuela Veloso
    • 2
  • Ramon López de Màntaras
    • 1
  • Carles Sierra
    • 1
  • Josep Lluís Arcos
    • 1
  1. 1.IIIA – Artificial Intelligence Research Institute, CSIC – Spanish Council for Scientific ResearchBarcelonaSpain
  2. 2.Computer Science DepartmentCarnegie Mellon UniversityPittsburghUSA

Personalised recommendations