Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Fuzzy Q-learning in a nondeterministic environment: developing an intelligent Ms. Pac-Man agent
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
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Fuzzy logic is a natural basis for modelling and solving problems involving imprecise knowledge and continuous systems. Unfortunately, fuzzy logic systems are invariably static (once created they do not change) and subjective (the creator imparts their beliefs on the system). In this paper we address the question of whether systems based on fuzzy logic can effectively adapt themselves to dynamic situations.To answer this question, we firstly design and implement an adaptive fuzzy logic agent for playing RoboCup soccer tournaments and then conduct a statistical analysis of the performance of the agent. We also extend the agent to incorporate adaptation in the cooperative situations that arise in this domain, and evaluate its performance under such conditions.Results from our analysis conclusively prove that our adaptive fuzzy logic agent can adapt rapidly and successfully to the changing dynamic situations with which it is presented.