Drew Mellor
ABSTRACT
Motivated by the intention to increase the expressive power of learning classifier systems, we developed a new Xcs derivative, Fox-cs, where the classifier and observation languages are a subset of first order logic. We found that Fox-cs was viable at tasks in two relational task domains, poker and blocks world, which cannot be represented easily using traditional bit-string classifiers and inputs. We also found that for these tasks, the level of generality obtained by Fox-cs in the portion of population that produces optimal behaviour is consistent with Wilson's generality hypothesis.
- M. Ahluwalia and L. Bull. A genetic programming-based classi . er system. In Banzhaf et al. {2} , pages 11--18.]]Google Scholar
- W. Banzhaf, J. Daida, A. E. Eiben, M. H. Garzon, V. Honavar, M. Jakiela, and R. E. Smith, editors. GECCO'99: Proceedings of the Genetic and Evolutionary Computation Conference Morgan Kaufmann, 1999.]]Google Scholar
- H. Blockeel and L. D. Raedt. Top-down induction of first-order logical decision trees. Artificial Intelligence 101(1 . 2):285--297, 1998.]]Google Scholar
- H. Blockeel, L. D. Raedt, N. Jacobs, and B. Demoen. Scaling up inductive logic programming by learning from interpretations. Data Mining and Knowledge Discovery 3(1):59--93, 1999.]] Google ScholarDigital Library
- L. B. Booker. Representing attribute-based concepts in a classifier system. In G. J. E. Rawlins, editor, Proceedings of the First Workshop on Foundations of Genetic Algorithms (FOGA91)pages 115--127, San-Maeto, 1991. Morgan Kaufmann.]]Google Scholar
- L. Bull and T. O 'Hara. Accuracy-based neuro and neuro-fuzzy classifier systems. In GECCO-2002: Proceedings of the Genetic and Evolutionary Computation Conference pages 905--911. Morgan Kaufmann, 2002.]]Google Scholar
- M. V. Butz and S. W. Wilson. An algorithmic description of XCS. Soft Computing 6(3--4):144--153, 2002.]]Google Scholar
- S. Dzeroski, L. D. Raedt, and K. Driessens. Relational reinforcement learning. Machine Learning 43(1--2):7--52, 2001.]]Google Scholar
- D. B. Fogel. Evolutionary Computation: Toward a New Philosophy of Machine Intelligence IEEE Press, 1995.]] Google ScholarDigital Library
- J. H. Holland. Escaping brittleness:the possibilities of general-purpose learning algorithms applied to parallel rule-based systems. In R. S. Michalski, J. G. Carbonell, and T. M. Mitchell, editors, Machine Learning, an Artificial Intelligence Approach pages 593--623, Los Altos, California, 1986. Morgan Kaufmann.]]Google Scholar
- W. V. Laer. From Propositional to First Order Logic in Machine Learning and Data Mining PhDthesis, Katholieke Universiteit Leuven, 2002.]]Google Scholar
- P. L. Lanzi. Extending the representation of classifier conditions, part II: From messy codings to S-expressions. In Banzhaf et al. {2} , pages 345--352.]]Google Scholar
- P. L. Lanzi, W. Stolzmann, and S. W. Wilson, editors. Learning Classifier Systems: from Foundations to Applications Springer-Verlag, 2000.]] Google ScholarDigital Library
- S. Muggleton. Inductive Logic Programming. In The MIT Encyclopedia of the Cognitive Sciences (MITECS) Academic Press, 1992.]]Google Scholar
- J. R. Quinlan. Learning logical definition from relations. Machine Learning 5(3):239--266, 1990.]] Google ScholarDigital Library
- J. D. Schaffer, editor. Proceedings of the Third International Conference on Genetic Algorithms San Mateo, CA, 1989. Morgan Kaufmann.]] Google ScholarDigital Library
- D. Schuurmans and J. Schaeffer. Representational difficulties with classifier systems. In Scha . er {16} , pages 328--333.]] Google ScholarDigital Library
- L. Shu and J. Schaeffer. VCS: Variable classiffer system. In Schaffer {16} , pages 334--339.]] Google ScholarDigital Library
- J. Slaney and S. Thiébaux. Blocks World revisited. Artificial Intelligence 125:119--153, 2001.]] Google ScholarDigital Library
- P. Tadepalli, R. Givan, and K. Driessens, editors. Proceedings of the ICML'04 Workshop on Relational Reinforcement Learning 2004. http://eecs. oregonstate. edu/research/rrl/index. html.]]Google Scholar
- C. Thornton. Truth from Trash: How Learning Makes Sense The MIT Press, 2000.]] Google ScholarDigital Library
- S. W. Wilson. Classifier . tness based on accuracy. Evolutionary Computation 3(2):149--175, 1995.]] Google ScholarDigital Library
- S. W. Wilson. Generalization in the XCS classifier system. In Genetic Programming 1998: Proceedings of the Third Annual Conference pages 665--674, University of Wisconsin, Madison, Wisconsin, USA, 1998. Morgan Kaufmann.]]Google Scholar
- S. W. Wilson. Get real! XCS with continuous-valued inputs. In Lanzi et al. {13} , pages 209--222.]] Google ScholarDigital Library
Index Terms
- A first order logic classifier system
Recommendations
Policy transfer with a relational learning classifier system
GECCO '05: Proceedings of the 7th annual workshop on Genetic and evolutionary computationPolicy transfer occurs when a system transfers a policy learnt for one task to another task with little or no retraining, and allows a system to perform robustly and learn efficiently, especially when the new task is more complex than the original task. ...
Learning classifier system ensemble for data mining
GECCO '05: Proceedings of the 7th annual workshop on Genetic and evolutionary computationThis paper proposes LCSE, a learning classifier system ensemble, which is an extension of the classical learning classifier system(LCS). The classical LCS includes two major modules, a genetic algorithm module used to facilitate rule discovery, and a ...
Comments