Incremental Generation of Abductive Explanations for Tactical Behavior

  • Thomas Wagner
  • Tjorben Bogon
  • Carsten Elfers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5001)


According to the expert literature on (human) soccer, e.g., the tactical behavior of a soccer team should differ significantly with respect to the tactics and strategy of the opponent team. In the offensive phase the attacking team is usually able to actively select an appropriate tactic with limited regard to the opponent strategy. In contrast, in the defensive phase the more passive recognition of tactical patterns of the behavior of the opponent team is crucial for success. In this paper we present a qualitative, formal, abductive approach, based on a uniform representation of soccer tactics that allows to recognize/explain the tactical and strategical behavior of opponent teams based on past (usually incomplete) observations.


Abductive Reasoning Soccer Team Plan Recognition Prime Implicants Opponent Team 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Albrecht, D.W., Zukerman, I., Nicholson, A.E.: Bayesian models for keyhole plan recognition in an adventure game. User Modeling and User-Adapted Interaction 8(1-2), 5–47 (1998)CrossRefGoogle Scholar
  2. 2.
    Appelt, D.E., Pollack, M.E.: Weighted abduction for plan ascription. User Modeling and User-Adapted Interaction 2(1-2), 1–25 (1991)Google Scholar
  3. 3.
    Avrahami-Zilberbrand, D., Kaminka, G.A.: Fast and complete symbolic plan recognition. In: IJCAI, pp. 653–658 (2005)Google Scholar
  4. 4.
    Drücker, C., Hübner, S., Visser, U., Weland, H.-G.: As time goes by - using time series based decision tree induction to analyze the behaviour of opponent players. In: Birk, A., Coradeschi, S., Tadokoro, S. (eds.) RoboCup 2001. LNCS (LNAI), vol. 2377, pp. 325–330. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  5. 5.
    Eiter, T., Makino, K.: On computing all abductive explanations. In: Eighteenth national conference on Artificial intelligence, Menlo Park, CA, USA, pp. 62–67. American Association for Artificial Intelligence (2002)Google Scholar
  6. 6.
    Hobbs, J.R., Stickel, M., Martin, P., Edwards, D.D.: Interpretation as abduction. In: 26th Annual Meeting of the Association for Computational Linguistics: Proceedings of the Conference, Buffalo, New York, pp. 95–103 (1988)Google Scholar
  7. 7.
    Intille, S., Bobick, A.: Recognizing planned, multi-person action. In: Computer Vision and Image Understanding, vol. 81, pp. 414–445 (2001)Google Scholar
  8. 8.
    Lattner, A.D., Miene, A., Visser, U., Herzog, O.: Sequential pattern mining for situation and behavior prediction in simulated robotic soccer. In: Bredenfeld, A., Jacoff, A., Noda, I., Takahashi, Y. (eds.) RoboCup 2005. LNCS (LNAI), vol. 4020, pp. 118–129. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  9. 9.
    Lucchesi, M.: Coaching the 3-4-1-2 and 4-2-3-1. Reedswain Publishing (2001)Google Scholar
  10. 10.
    Miene, A., Visser, U.: Interpretation of spatio-temporal relations in real-time and dynamic environments. In: Birk, A., Coradeschi, S., Tadokoro, S. (eds.) RoboCup 2001. LNCS (LNAI), vol. 2377, pp. 441–447. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  11. 11.
    Ng, H.T., Mooney, R.J.: On the role of coherence in abductive explanation. In: National Conference on Artificial Intelligence, pp. 337–342 (1990)Google Scholar
  12. 12.
    Peirce, C.S.: Collected Papers of Charles Sanders Peirce. Harvard University Press (1931)Google Scholar
  13. 13.
    Selman, B., Levesque, H.J.: Support set selection for abductive and default reasoning. Artif. Intell. 82(1-2), 259–272 (1996)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Thomas Wagner
    • 1
  • Tjorben Bogon
    • 1
  • Carsten Elfers
    • 1
  1. 1.Center for Computing Technologies (TZI)Universität BremenBremen

Personalised recommendations