Adding temporary memory to ZCS
Adaptive Behavior
Adaptive Behavior
Transition network grammars for natural language analysis
Communications of the ACM
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
A Tale of Two Classifier Systems
Machine Learning
A Comparison Between ATNoSFERES And XCSM
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Latent Learning and Action Planning in Robots with Anticipatory Classifier Systems
Learning Classifier Systems, From Foundations to Applications
Memory Approaches to Reinforcement Learning in Non-Markovian Domains
Memory Approaches to Reinforcement Learning in Non-Markovian Domains
Reinforcement learning with selective perception and hidden state
Reinforcement learning with selective perception and hidden state
Finite-memory control of partially observable systems
Finite-memory control of partially observable systems
Toward Optimal Classifier System Performance in Non-Markov Environments
Evolutionary Computation
Memory exploitation in learning classifier systems
Evolutionary Computation
Classifier fitness based on accuracy
Evolutionary Computation
Learning finite-state controllers for partially observable environments
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
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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.