A Bayesian approach to learning classifier systems in uncertain environments
Davide Aliprandi, Alex Mancastroppa, Matteo Matteucci · 2006
In this paper we propose a Bayesian framework for XCS [9], called BXCS. Following [4], we use probability distributions to represent the uncertainty over the classifier estimates of payoff. A novel interpretation of classifier and an extension of the accuracy concept are presented. The probabilistic approach is aimed at increasing XCS learning capabilities and tendency to evolve accurate, maximally general classifiers, especially when uncertainty affects the environment or the reward function. We show that BXCS can approximate optimal solutions in stochastic environments with a high level of uncertainty.