Discovering tactical behavior patterns supported by topological structures in soccer agent domains

ABSTRACT

Behaviors in soccer-agent domains can involve individual plays, several players involved in tactical plays or the whole team trying to follow strategies supported by specific formations. The discovery of such behaviors needs the tracking of both the positions of players at any instant of the game and relevant relations able to represent particular interactions between players. Nevertheless, the tracking task becomes very complicated because the dynamic conditions of the game implying drastic changes of positions and interactions between players. We propose in this work a model able to manage the constant changes occurring in the game, which consists in building topological structures based on triangular planar graphs. Thus, based on this model tactical behavior patterns have been discovered even the dynamic conditions. Experimental results show that the proposed model is able to manage the constant changes of the world and discover tactical behaviors patterns. For that, an important number of matches have been analyzed from the RoboCup Simulation league.

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Index Terms

• Computing methodologies

  • Artificial intelligence

    • Distributed artificial intelligence

      • Intelligent agents

    • Planning and scheduling

    • Search methodologies

      • Discrete space search

      • Game tree search

  • Machine learning

• Information systems

  • Information systems applications

    • Data mining

• Mathematics of computing

  • Discrete mathematics

    • Graph theory

      • Graph algorithms