Evolving Fuzzy Rules for Goal-Scoring Behaviour in a Robot Soccer Environment

The ability to construct autonomous robots that are able to learn from the environment in which they operate in order to achieve their objectives is a need so far largely unsatisfied, especially for dynamic environments which change quickly and are noisy and uncertain. A method of developing controllers for simple robots that learn, via artificial evolution, how to react in the noisy, uncertain and dynamic environment of simulated robot soccer in order to achieve goalscoring behaviour is investigated by this thesis. A rules-based architecture that uses a fuzzy-logic inferencing system is proposed for the simulated soccer player. The set of rules that controls the behaviour of the player is developed by evolving a population of simulated soccer-playing robots that are evaluated in the robot soccer environment. The evolutionary algorithm implemented to evolve the rules is a messycoded genetic algorithm. The soccer simulation environment chosen for this work is the RoboCup Soccer Simulation League, which is a dynamic, noisy, real-time environment specifically developed for artificial intelligence research. However, because the RoboCup simulator is a real-time environment all training and testing in the environment takes place in real-time, and this has a significant impact on the capacity of the method to do any real learning. The client-server architecture of the RoboCup simulator further complicates the implementation of the learning process. To overcome these impediments a less complex model of the RoboCup simulator was created. The new simulator, named SimpleSoccer, is a multi-player capable, dynamic environment that is not noisy, does not operate in real-time, and does not implement a client-server architecture. The simplified environment of SimpleSoccer allows the evolutionary process to run much faster than in the RoboCup environment, so real learning can take place in more reasonable timeframes. Tests are performed to ensure that the SimpleSoccer environment is a