- author = "Jeff Riley",
- title = "Evolving Fuzzy Rules for Goal-Scoring Behaviour in a Robot Soccer Environment",
- school = "School of Computer Science and Information Technology, RMIT",
- year = "2005",
- address = "Australia",
- month = dec,
- keywords = "genetic algorithms, genetic programming",
-
URL = "
http://goanna.cs.rmit.edu.au/~vc/papers/riley-phd.pdf",
- size = "278 pages",
-
abstract = "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 goal scoring behaviour is investigated by this
thesis.
A rule-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 messy coded 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 time frames. Tests are performed to ensure that the SimpleSoccer environment is a sufficiently good model of the RoboCup environment and that rules learned in the simpler environment are transferable to the RoboCup environment. A method of accelerating the evolutionary search in the RoboCup environment by seeding the population with rules learned in the SimpleSoccer environment is demonstrated.
This thesis also examines the question of how human expertise and expert knowledge affects the evolutionary search. Developing good soccer-playing skills for the robot soccer environment is known to be a difficult problem for evolutionary algorithms, and the problem is often solved by giving players some innate, hand-coded skills to increase the probability that the players will achieve the overall objective set. A well designed fitness function for the evolutionary algorithm can artificially guide the evolutionary process by rewarding incremental and intermediate solutions. Tests are conducted to determine how varying the amount of human help given to the evolutionary algorithm affects the result of the evolutionary process.
Finally, the thesis investigates the underlying cause of the difficulty of the robot soccer problem for evolutionary algorithms. A systematic study of the problem search spaces and fitness landscapes is presented which provides a good understanding of why the problem is difficult, and how injecting human expertise and expert knowledge in various ways can change the relative difficulty of the problem. The study also leads to the conjecture that there is an inherent limit to the amount of learning possible by evolutionary algorithms.",
