A Bayesian approach to learning classifier systems in uncertain environments

Authors:
Davide Aliprandi;Alex Mancastroppa;Matteo Matteucci
Affiliations:
Politecnico di Milano, via Ponzio, Milan;Politecnico di Milano, via Ponzio, Milan;Politecnico di Milano, via Ponzio, Milan
Venue:
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Year:
2006

Citing 4
Cited 0

Bayesian Q-learning

AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Introduction to Reinforcement Learning

Introduction to Reinforcement Learning
Classifier fitness based on accuracy

Evolutionary Computation
Toward a theory of generalization and learning in XCS

IEEE Transactions on Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.