An Experimental Comparison between ATNoSFERES and ACS

Samuel Landau 1 Olivier Sigaud 1 Sébastien Picault 2, 3 Pierre Gérard 1
1 OASIS - Objets et Agents pour Systèmes d'Information et Simulation
LIP6 - Laboratoire d'Informatique de Paris 6
3 SMAC - Systèmes Multi-Agents et Comportements
CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Abstract : After two papers comparing ATNoSFERES with XCSM, a Learning Classifier System with internal states, this paper is devoted to a comparison between ATNoSFERES and ACS (an Anticipatory Learning Classifier System). As previously, we focus on the way perceptual aliazing problems encountered in non-Markov environments are solved with both kinds of systems. We shortly present ATNoSFERES, a framework based on an indirect encoding Genetic Algorithm which builds finite-state automata controllers, and we compare it with ACS through two benchmark experiments. The comparison shows that the difference in performance between both system depends on the environment. This raises a discussion of the adequacy of both adaptive mechanisms to particular subclasses of non-Markov problems. Furthermore, since ACS converges much faster than ATNoSFERES, we discuss the need to introduce learning capabilities in our model. As a conclusion, we advocate for the need of more experimental comparisons between different systems in the Learning Classifier System community.
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Samuel Landau, Olivier Sigaud, Sébastien Picault, Pierre Gérard. An Experimental Comparison between ATNoSFERES and ACS. 6th International Workshop on Learning Classifier Systems (IWLCS'2003), 2003, Chicago, United States. pp.144-160. ⟨hal-00860490⟩

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