Will Browne
ABSTRACT
Abstraction is a higher order cognitive ability that facilitates the production of rules that are independent of their associations. Experience from real-world data-mining has shown the need for such higher level rules. The game of Connect 4 is both multistep and complex, so standard Q-learning and Learning Classifier Systems perform poorly. The introduction of a novel Abstraction algorithm into an LCS is shown to improve performance in the evolution of playing strategies.
- Browne, W. N. L., The development of an industrial learning classifier system for data-mining in a steel hot strip mill. In Bull, L. Applications of Learning Classifier Systems. Springer, Berlin, 2004, 223--259.Google Scholar
- Oxford English dictionary, http://www.oed.com/.Google Scholar
- Thornton. C., Quantitative abstraction theory. In Artificial intelligence and the simulation of behaviour. 1, 3 (2003) 281--290.Google Scholar
- Sutton, R. S., Precup, D., and Singh, S. Between MDPs and Semi-MDPs: a framework for temporal abstraction in reinforcement learning. Artificial intelligence. 112, 1-2 (1999), 181--211. Google ScholarDigital Library
- Miller, G.A. The magical number seven, plus or minus two; Some limits on our capacity for processing information. Psychological Review, 63, (1956), 81--97.Google ScholarCross Ref
- Butz, M., and Wilson, S. W. An algorithmic description of XCS. In Soft Computing: a fusion of foundations, methodologies and applications, 6 (2002), 162--170.Google Scholar
- Holland, J. H. adaptation in natural and artificial systems. Ann Arbor, MI: University of Michigan press, 1975. Google ScholarDigital Library
- Butz, M., Rule-base evolutionary online learning systems: learning bounds, classification and prediction. PhD thesis University of Illinois, Illinois, 2004. Google ScholarDigital Library
- Allis, V. A Knowledge Based Approach of Connect 4. Masters Thesis, Vrije Universiteit, Netherlands, 1988.Google Scholar
- Watkins, C. J. C. H. Learning from Delayed Rewards. PhD thesis, King's College, Cambridge, England, 1989.Google Scholar
- Lanzi, P-L., Learning classifier systems from a reinforcement learning perspective. In Soft Computing: a fusion of foundations, methodologies and applications, 6 (2002), 162--170.Google Scholar
- Sutton, R. S., and Barto, A. G. Reinforcement learning: An introduction. Cambridge, MA: MIT Press, 1998. Google ScholarDigital Library
Index Terms
- An abstraction agorithm for genetics-based reinforcement learning
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