Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Applications of Learning Classifier Systems
Applications of Learning Classifier Systems
Zcs: A zeroth level classifier system
Evolutionary Computation
Classifier fitness based on accuracy
Evolutionary Computation
ACM SIGEVOlution
Learning to play using low-complexity rule-based policies: illustrations through Ms. Pac-Man
Journal of Artificial Intelligence Research
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RAMP is a rule-based agent for playing Ms. Pac-Man according to the rules stipulated in the 2008 World Congress on Computational Intelligence Ms. Pac-Man Competition. During the competition, our highest score was 15,970, outscoring the eleven other entrants in the competition. In runs reported here, RAMP achieves an average score over 10,000 and a high score of 18,560 across 100 runs; the highest score RAMP has achieved to date is 19,000. These are scores that are better than typical human novice players, including the paper authors themselves. The system was designed to have an evolutionary component, however, this was not developed in time for the competition, which instead used hand-coded rules. We have found the process of tuning the rule sets and accompanying parameters to be a time consuming and inexact process that is expected to benefit from an evolutionary computation approach. This paper describes our initial implementation as well as our progress towards adding an evolutionary computation component to enable the agent learn to play the game.