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2006
Springer

A Bayesian approach to learning classifier systems in uncertain environments

10 years 8 months ago
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
Davide Aliprandi, Alex Mancastroppa, Matteo Matteu
Added 23 Aug 2010
Updated 23 Aug 2010
Type Conference
Year 2006
Where GECCO
Authors Davide Aliprandi, Alex Mancastroppa, Matteo Matteucci
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