Classifier systems and genetic algorithms
Artificial Intelligence
Lookahead planning and latent learning in a classifier system
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Adding temporary memory to ZCS
Adaptive Behavior
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Integrating Unsupervised Learning, Motivation and Action Selection in an A-life Agent
ECAL '99 Proceedings of the 5th European Conference on Advances in Artificial Life
Investigating Generalization in the Anticipatory Classifier System
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Dynamic Programming
Genetic and non-genetic operators in alecsys
Evolutionary Computation
Zcs: A zeroth level classifier system
Evolutionary Computation
Classifier fitness based on accuracy
Evolutionary Computation
An analysis of generalization in the xcs classifier system
Evolutionary Computation
Being Reactive by Exchanging Roles: An Empirical Study
Balancing Reactivity and Social Deliberation in Multi-Agent Systems, From RoboCup to Real-World Applications (selected papers from the ECAI 2000 Workshop and additional contributions)
Using Classifier Systems as Adaptive Expert Systems for Control
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
Biasing Exploration in an Anticipatory Learning Classifier System
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
On Lookahead and Latent Learning in Simple LCS
Learning Classifier Systems
Analysing Learning Classifier Systems in Reactive and Non-reactive Robotic Tasks
Learning Classifier Systems
Dynamical genetic programming in xcsf
Evolutionary Computation
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This paper describes our work on the use of anticipation in Learning Classifier Systems (LCS) applied to Markov problems. We present YACS1, a new kind of Anticipatory Classifier System. It calls upon classifiers with a [Condition], an [Action] and an [Effect] part. As in the traditional LCS framework, the classifier discovery process relies on a selection and a creation mechanism. As in the Anticipatory Classifier System (ACS), YACS looks for classifiers which anticipate well rather than for classifiers which propose an optimal action. The creation mechanism does not rely on classical genetic operators but on a specialization operator, which is explicitly driven by experience. Likewise, the action qualities of the classifiers are not computed by a classical bucket-brigade algorithm, but by a variety of the value iteration algorithm that takes advantage of the effect part of the classifiers. This paper presents the latent learning process of YACS. The description of the reinforcement learning process is focussed on the problem induced by the joint use of generalization and dynamic programming methods.