Use of Fuzzy Histograms to Model the Spatial Distribution of Objects in Case-Based Reasoning

  • Alan Davoust
  • Michael W. Floyd
  • Babak Esfandiari
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5032)


In the context of the RoboCup Simulation League, we describe a new representation of a software agent’s visual perception (“scene”), well suited for case-based reasoning.

Most existing representations use either heterogeneous, manually selected features of the scene, or the raw list of visible objects, and use ad hoc similarity measures for CBR. Our representation is based on histograms of objects over a partition of the scene space. This method transforms a list of objects into an image-like representation with customizable granularity, and uses fuzzy logic to smoothen boundary effects of the partition. We also introduce a new similarity metric based on the Jaccard Coefficient, to compare scenes represented by such histograms.

We present our implementation of this approach in a case-based reasoning project, and experimental results showing highly efficient scene comparison.


Case Based Reasoning Fuzzy Histograms Knowledge Representation Soccer Simulation 


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  1. 1.
    Lam, K., Esfandiari, B., Tudino, D.: A scene-based imitation framework for Robocup clients. In: MOO Modeling Others from Observation, AAAI workshop (2006)Google Scholar
  2. 2.
    Floyd, M., Esfandiari, B., Lam, K.: A Case-based Reasoning Approach to Imitating RoboCup Players. In: Proceedings of FLAIRS-2008, Florida AI Research Symposium (to appear, 2008)Google Scholar
  3. 3.
  4. 4.
    Wendler, J., Lenz, M.: CBR for Dynamic Situation Assessment in an Agent-Oriented Setting. In: Aha, D., Daniels, J.J. (eds.) Proc. AAAI 1998 Workshop on Case Based Reasoning Integrations, Madison, USA (1998)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 (LNAI), vol. 3020, pp. 739–747. Springer, Heidelberg (2004)Google Scholar
  6. 6.
    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
  7. 7.
    Ros, R., Veloso, M., López de Mántaras, R., Sierra, C., Arcos, J.L.: Retrieving and Reusing Game Plays for Robot Soccer. In: Roth-Berghofer, T.R., Göker, M.H., Güvenir, H.A. (eds.) ECCBR 2006. LNCS (LNAI), vol. 4106, pp. 47–61. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  8. 8.
    Moravec, H.P., Elfes, A.: High resolution maps from wide angle sonar. In: Proc. IEEE Int. Conf. Robotics and Automation, pp. 116–121 (1985)Google Scholar
  9. 9.
    Dubois, D., Prade, H.: Fuzzy Sets and Systems, theory and applications. Academic Press, New York (1980)zbMATHGoogle Scholar
  10. 10.
    Swain, M., Ballard, D.: Color indexing. International Journal of Computer Vision 7(1), 11–32 (1991)CrossRefGoogle Scholar
  11. 11.
    Strehl, A., Ghosh, J.: Value-based customer grouping from large retail data-sets. In: Proceedings of the SPIE Conference on Data Mining and Knowledge Discovery, Orlando, Florida, April 24-25, vol. 4057, pp. 33–42. SPIE (2000)Google Scholar
  12. 12.
    Haveliwala, T., Gionis, A., Klein, D., Indyk, P.: Similarity Search on the Web: Evaluation and Scalability Considerations, Stanford Technical Report (2000)Google Scholar
  13. 13.
    Langner, K.: The Krislet Java Client (1999),

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Alan Davoust
    • 1
  • Michael W. Floyd
    • 1
  • Babak Esfandiari
    • 1
  1. 1.Department of Systems and Computer EngineeringCarleton UniversityOttawa

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