Enhancing Artificial Intelligence in Games by Learning the Opponent’s Playing Style

  • Fabio Aiolli
  • Claudio Enrico Palazzi
Part of the IFIP International Federation for Information Processing book series (IFIPAICT, volume 279)


As virtual environments are becoming graphically nearly realistic, the need for a satisfying Artificial Intelligence (AI) is perceived as more and more important by game players. In particular, what players have to face nowadays in terms of AI is not far from what was available at the beginning of the video games era. Even nowadays, the AI of almost all games is based on a finite set of actions/reactions whose sequence can be easily predicted by expert players. As a result, the game soon becomes too obvious to still be fun. Instead, machine learning techniques could be employed to classify a player’s behavior and consequently adapt the game’s AI; the competition against the AI would become more stimulant and the fun of the game would last longer. To this aim, we consider a game where both the player and the AI have a limited information about the current game state and where it is part of the game to guess the information hidden by the opponent. We demonstrate how machine learning techniques could be easily implemented in this context to improve the AI by making it adaptive with respect to the strategy of a specific player.


Machine Learning Technique Game State Human Player Game Session Opponent Player 
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.


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Copyright information

© IFIP International Federation for Information Processing 2008

Authors and Affiliations

  • Fabio Aiolli
    • 1
  • Claudio Enrico Palazzi
    • 1
  1. 1.Pure and Applied Math DepartmentUniversity of PadovaPadovaItaly

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