Evolving robot strategy for open ended game

  • Tomonori Sugiyama
  • Takashi Kido
  • Masakazu Nakanishi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 956)


A good entertaiment must be interesting. This paper asserts that interesting games have unpredictability. Recent technologies in artificial life give us new possibility of unpredictability, such as the evolution of strategies of opponents. This paper seeks this possibility in a robot battle game using genetic algorithm for the evolution of strategies. We made a robot battle game called X-Window Robot Battle (XRB), a fighting game of two robots. Each robot's strategy is given by assembly language codes, and these codes are created by a user or a computer. We use genetic algorithms (GA) to evolve a robot's codes made by computer and make it possible for the robot to acquire a useful strategy without being explicitly programmed. The change of strategy of an opponent makes the game more unpredictable and interesting, thus we can enjoy the game. We believe that our attempt contributes to the entertainment industries.


Genetic Algorithm Artificial Life Uncertain Factor Assembly Code Entertainment Industry 
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 1995

Authors and Affiliations

  • Tomonori Sugiyama
    • 1
  • Takashi Kido
    • 1
  • Masakazu Nakanishi
    • 1
  1. 1.Department of MathematicsKeio UniversityKanagawa, 223Japan

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