Strategy construction using genetic algorithms for a real-time strategy game

Authors

Zinsser Angelo J. Babao, De La Salle University, Manila
Arturo Mariano N. Locsin, De La Salle University, Manila
Teresita C. Limoanco, De La Salle University, Manila
Sterling Ian K. Ty, De La Salle University, Manila
Ralph Edmond B. Mercado, De La Salle University, Manila
Paul Salvador B. Inventado, De La Salle University, Manila

College

College of Computer Studies

Department/Unit

Software Technology

Document Type

Archival Material/Manuscript

Publication Date

2007

Abstract

Games are good domains for strategy and decision making problems due to its unpredictability. Real-time strategy games require decision making in every situation. However, most implementation of computer opponents use hard-coded rules, making the game repetitive and predictable. Thus, experienced human players eventually learn and formulate strategies to exploit this weakness. This research has investigated the use of a machine learning algorithm, specifically Genetic Algorithm, in creating strategies for the computer opponent to achieve a degree of unpredictability in a real-time strategy game. Computer player's strategies are generated through evolution on already existing strategies. Each new strategy is given a fitness score by using them in simulations of scenarios commonly encountered in game. Evolution of the strategies is done continuously until an acceptable fitness score is achieved. Acceptable strategies are then used by the computer player in the actual game.

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Recommended Citation

Babao, Z. J., Locsin, A. N., Limoanco, T. C., Ty, S. K., Mercado, R. B., & Inventado, P. B. (2007). Strategy construction using genetic algorithms for a real-time strategy game. Retrieved from https://animorepository.dlsu.edu.ph/faculty_research/7426

Disciplines

Software Engineering

Keywords

Genetic algorithms; Computer games—Design

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