Evolving robot strategy for open ended game
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.
KeywordsGenetic Algorithm Artificial Life Uncertain Factor Assembly Code Entertainment Industry
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- 1.Tomonori Sugiyama, Takashi Kido, Mutsuhiro Yonezu, Junya Tsutsumi, Fumihiko Yamaguchi, Masaaki, Hayashi, Masakazu Nakanishi, Program Synthesis for Robot Wor using Genetic Algorithm. Workshop on Artificial Intelligence and Artificial Life for Entertainment 1994.Google Scholar
- 2.David E. Goldberg, Genetic Algorithms in Search, Optimization Machine Learning. Addison-Wesley, Reading, Mass, 1989.Google Scholar
- 3.J. H. Holland, Adaptation in natural and artificial systems The University of Michigan Press, 1975.Google Scholar
- 4.John R. Koza, Genetic programming The MIT Press, 1992.Google Scholar
- 5.R. Axelrod and D. Dion. The Further Evolution of Cooperation, Science, vol. 242, pages 1385–1390, December 1988.Google Scholar
- 6.K. Lindgren. Evolutionary Phenomena in Simple Dynamics In C. Langton, C. Taylor, J.D. Farmer, and S. Rasmussen, editors, Artificial Life II. SFI Studies in the Science of Complexity, 1991.Google Scholar