Sequential Pattern Mining for Situation and Behavior Prediction in Simulated Robotic Soccer

  • Andreas D. Lattner
  • Andrea Miene
  • Ubbo Visser
  • Otthein Herzog
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4020)


Agents in dynamic environments have to deal with world representations that change over time. In order to allow agents to act autonomously and to make their decisions on a solid basis an interpretation of the current scene is necessary. If intentions of other agents or events that are likely to happen in the future can be recognized the agent’s performance can be improved as it can adapt the behavior to the situation. In this work we present an approach which applies unsupervised symbolic learning off-line to a qualitative abstraction in order to create frequent patterns in dynamic scenes. These patterns can be later applied during runtime in order to predict future situations and behaviors. The pattern mining approach was applied to two games of the 2D RoboCup simulation league.


Association Rule Temporal Relation Frequent Pattern Pattern Mining Prediction Rule 
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.
    Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, DC, pp. 207–216 (May 1993)Google Scholar
  2. 2.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, pp. 487–499 (September 1994)Google Scholar
  3. 3.
    Allen, J.F.: Maintaining knowledge about temporal intervals. Communications of the ACM 26(11), 832–843 (1983)MATHCrossRefGoogle Scholar
  4. 4.
    Allen, J.F.: Towards a general theory of action and time. Artificial Intelligence 23(2), 123–154 (1984)MATHCrossRefGoogle Scholar
  5. 5.
    Frank, I., Tanaka-Ishi, K., Arai, K., Matsubara, H.: The statistics proxy server. In: Balch, G.T., Stone, P., Kraetschmar (eds.) 4th International Workshop on RoboCup, Melbourne, Australia, pp. 199–204. Carnegie Mellum University Press (2000)Google Scholar
  6. 6.
    Freksa, C.: Temporal reasoning based on semi-intervals. Artificial Intelligence 54(1–2), 199–227 (1992)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Hernández, D., Clementini, E., Di Felice, P.: Qualitative distances. In: Kuhn, W., Frank, A.U. (eds.) COSIT 1995. LNCS, vol. 988. Springer, Heidelberg (1995)Google Scholar
  8. 8.
    Höppner, F.: Learning temporal rules from state sequences. In: Proceedings of the IJCAI 2001 Workshop on Learning from Temporal and Spatial Data, Seattle, USA, pp. 25–31 (2001)Google Scholar
  9. 9.
    Huang, Z., Yang, Y., Chen, X.: An approach to plan recognition and retrieval for multi-agent systems. In: Prokopenko, M. (ed.) Workshop on Adaptability in Multi-Agent Systems, First RoboCup Australian Open 2003 (AORC 2003), Sydney, Australia, CSIRO (2003)Google Scholar
  10. 10.
    Kaminka, G., Fidanboylu, M., Chang, A., Veloso, M.: Learning the sequential coordinated behavior of teams from observation. In: Kaminka, G.A., Lima, P.U., Rojas, R. (eds.) RoboCup 2002. LNCS (LNAI), vol. 2752, pp. 111–125. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  11. 11.
    Koperski, K., Han, J.: Discovery of spatial association rules in geographic information databases. In: Proceedings of the 4th International Symposium on Advances in Spatial Databases, SSD, Portland, Maine, pp. 47–66 (1995)Google Scholar
  12. 12.
    Kuhlmann, G., Stone, P.: Progress in learning 3 vs. 2 keepaway. In: Polani, D., Browning, B., Bonarini, A., Yoshida, K. (eds.) RoboCup 2003. LNCS, vol. 3020, pp. 694–702. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  13. 13.
    Lattner, A.D., Herzog, O.: Unsupervised learning of sequential patterns. In: ICDM 2004 Workshop on Temporal Data Mining: Algorithms, Theory and Applications (TDM 2004), Brighton, UK, November 1 (2004)Google Scholar
  14. 14.
    Malerba, D., Lisi, F.A.: An ILP method for spatial association rule mining. In: Working notes of the First Workshop on Multi-Relational Data Mining, Freiburg, Germany, pp. 18–29 (2001)Google Scholar
  15. 15.
    Mannila, H., Toivonen, H., Verkamo, A.I.: Discovery of frequent episodes in event sequences. Data Mining and Knowledge Discovery 1, 259–289 (1997)CrossRefGoogle Scholar
  16. 16.
    Mennis, J., Liu, J.W.: Mining association rules in spatio-temporal data. In: Proceedings of the 7th International Conference on GeoComputation, University of Southampton, UK, September 8-10 (2003)Google Scholar
  17. 17.
    Merke, A., Riedmiller, M.: Karlsruhe brainstormers - A reinforcement learning approach to robotic soccer. In: Birk, A., Coradeschi, S., Tadokoro, S. (eds.) RoboCup 2001. LNCS, vol. 2377, p. 435. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  18. 18.
    Miene, A., Lattner, A.D., Visser, U., Herzog, O.: Dynamic-preserving qualitative motion description for intelligent vehicles. In: Proceedings of the IEEE Intelligent Vehicles Symposium (IV 2004), pp. 642–646, June 14-17 (2004)Google Scholar
  19. 19.
    Miene, A., Visser, U., Herzog, O.: Recognition and prediction of motion situations based on a qualitative motion description. In: Polani, D., Browning, B., Bonarini, A., Yoshida, K. (eds.) RoboCup 2003. LNCS, vol. 3020, pp. 77–88. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  20. 20.
    Riley, P., Veloso, M.: Recognizing probabilistic opponent movement models. In: Birk, A., Coradeschi, S., Tadokoro, S. (eds.) RoboCup 2001. LNCS, vol. 2377, pp. 453–458. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  21. 21.
    Riley, P., Veloso, M.M.: Coaching advice and adaptation. In: Polani, D., Browning, B., Bonarini, A., Yoshida, K. (eds.) RoboCup 2003. LNCS, vol. 3020, pp. 192–204. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  22. 22.
    Stone, P., Sutton, R.S.: Scaling reinforcement learning toward robocup soccer. In: Proceedings of the 18th International Conference on Machine Learning (2001)Google Scholar
  23. 23.
    Stone, P., Veloso, M.: A layered approach to learning client behaviors in the robocup soccer server. Applied Artificial Intelligence 12, 165–188 (1998)CrossRefGoogle Scholar
  24. 24.
    Visser, U., Drücker, C., Hübner, S., Schmidt, E., Weland, H.-G.: Recognizing formations in opponent teams. In: Stone, P., Balch, T., Kraetzschmar, G.K. (eds.) RoboCup 2000. LNCS, vol. 2019, pp. 391–396. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  25. 25.
    Visser, U., Weland, H.-G.: Using online learning to analyze the opponent’s behavior. In: Kaminka, G.A., Lima, P.U., Rojas, R. (eds.) RoboCup 2002. LNCS (LNAI), vol. 2752, pp. 78–93. Springer, Heidelberg (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Andreas D. Lattner
    • 1
  • Andrea Miene
    • 1
  • Ubbo Visser
    • 1
  • Otthein Herzog
    • 1
  1. 1.Center for Computing Technologies – TZIUniversitaet BremenBremenGermany

Personalised recommendations