Adaptive automata model for learning opponent behavior based on genetic algorithms

摘要

The purpose of this research is to study how genetic algorithms (GA's) are applied in the field of Game Theory. GA's are effective approaches for machine learning and optimization problems. In this work, genetic algorithm is utilized to determine the behavior of an opponent in Prisoners’ Dilemma. The opponent behavior will be modeled by means of adaptive automaton. The basic problem of this study is the well-known Prisoner Dilemma. The primary purpose of this research is to determine the opponent behavior towards finding a better strategy to be followed by the player, since the best strategy to be followed depends on the opponent behavior. The results of our proposed model showed the capability of our model to identify the opponent model efficiently. Based on the provided knowledge about the opponent model, the dynamic strategy showed better results when compared to other well-known strategies.