A Bayesian approach to learning classifier systems in uncertain environments

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. Categories and Subject Descriptors: I.2.6 [Computing Methodologies]: Artificial Intelligence

• Type: Conference
• Year: 2006
• Where: GECCO
• Authors: Davide Aliprandi, Alex Mancastroppa, Matteo Matteucci
• Keywords: Bayesian Framework, Classifier Estimates, GECCO 2006, Optimization, XCS Learning Capabilities