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

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

  author =       "Jeff Riley",
  title =        "Evolving Fuzzy Rules for Goal-Scoring Behaviour in a
                 Robot Soccer Environment",
  school =       "School of Computer Science and Information Technology,
  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

                 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.",

Genetic Programming entries for Jeff Riley