Éditeur(s) : HAL CCSD ACM
Résumé : International audience
In this study, we use an adapted version of Pitsburgh-like learning classifier system to perform over classification tasks. The Adapted Pittsburgh Classifier System, enhanced with a new mechanism, allows us to consider the classification problems and their treatment by a given LCS in a different manner. Our aim is to exhibit elements in order to prove that, using the action covering mechanism, this system is able to build an inner map of a given classification learning sample. In the context of this paper, this map is built using an intrisic property of Pittsburgh-like CS: the use of various collections of classifiers amongst a unique population.
Proceedings of the 12th annual conference comp on Genetic and evolutionary computation
Portland,Oregon, United States

hal-00520609
https://hal.archives-ouvertes.fr/hal-00520609
DOI : 10.1145/1830761.1830823

Éditeur(s) : HAL CCSD
Résumé : International audience
This paper focuses on the study of the influence of a newly implemented mechanism on a Pittsburgh-like classifier system. The Adapted Pittsburgh Classifier System is a learning classifier system that uses genetic algorithms to evolve its ruleset. The new mechanism discussed is inspired from Wilson work on the eXtended Classifier System (XCS): it allows the concerned LCS to adapt its rule set when facing a new signal by modifying an existing rule. This mechanism is called covering mechanism due to the fact that the rule is covered up by a new rule which is sensitive to this new signal. Effects of this covering mechanism are first measured on the performances of APCS on well known multi-step environments: maze-type environments (Woods 101 and E2). In addition, further measures presented in this paper indicate that we could possibly rely on the behavior of this covering mechanism to automate the research of a correct cognitive capacity needed by the APCS to solve a given multistep problem.
2008 Eighth International Conference on Hybrid Intelligent Systems
Barcelone, Spain

hal-00520608
https://hal.archives-ouvertes.fr/hal-00520608
DOI : 10.1109/HIS.2008.105

Éditeur(s) : HAL CCSD ACM New York, NY, USA
Résumé : International audience
This paper focuses on the study of the behavior of a genetic algorithm based classifier system, the Adapted Pittsburgh Classifier System (A.P.C.S), on maze type environments containing aliasing squares. This type of environment is often used in reinforcement learning litterature to assess the performances of learning methods when facing problems containing non markov situations. Through this study, we discuss on the performances of the APCS on maze type environments and also of the efficiency of an improvement of the APCS learning method inspired from the XCS : the covering mechanism. We manage to show that, without any memory mechanism, the APCS is able to build and to keep accurate strategies to produce regular sub-optimal solutions to these maze problem. This statement is shown through a comparison of the results obtained by the XCS, XCSM and XCSMH on distinct maze problems with these obtained by the APCS.
Proceedings of the 2008 GECCO conference companion on Genetic and evolutionary computation
Atlanta, GA, United States

hal-00520607
https://hal.archives-ouvertes.fr/hal-00520607
DOI : 10.1145/1388969.1389013

Éditeur(s) : HAL CCSD
Résumé : International audience
This paper focuses on the study of the behaviour of a singular classifier system, the Adapted Pittsburgh Classifier System (A.P.C.S), on environments containing aliasing situations. Maze type environments are often used in reinforcement learning literature to assess the performances of learning methods when facing problems containing non Markovian situations. Those situations are often encountered when performing reinforcement learning on aliased data samples issued from meteorological simulations.
International Journal of Artificial Intelligence

hal-00520611
https://hal.archives-ouvertes.fr/hal-00520611