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Evolving Behaviour Trees for the Commercial Game DEFCON

  • Chong-U Lim
  • Robin Baumgarten
  • Simon Colton
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6024)

Abstract

Behaviour trees provide the possibility of improving on existing Artificial Intelligence techniques in games by being simple to implement, scalable, able to handle the complexity of games, and modular to improve reusability. This ultimately improves the development process for designing automated game players. We cover here the use of behaviour trees to design and develop an AI-controlled player for the commercial real-time strategy game DEFCON. In particular, we evolved behaviour trees to develop a competitive player which was able to outperform the game’s original AI-bot more than 50% of the time. We aim to highlight the potential for evolving behaviour trees as a practical approach to developing AI-bots in games.

Keywords

Genetic Programming Finite State Machine Behaviour Tree Human Player Game Developer 
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

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Chong-U Lim
    • 1
  • Robin Baumgarten
    • 1
  • Simon Colton
    • 1
  1. 1.Computational Creativity Group, Department of ComputingImperial CollegeLondon

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