Learning to Perceive and Act by Trial and Error
Machine Learning
Technical Note: \cal Q-Learning
Machine Learning
Reinforcement learning with replacing eligibility traces
Machine Learning - Special issue on reinforcement learning
Fuzzy set theory: foundations and applications
Fuzzy set theory: foundations and applications
Anytime learning and adaptation of structured fuzzy behaviors
Adaptive Behavior - Special issue on environment structure and behavior
Robot Shaping: An Experiment in Behavior Engineering
Robot Shaping: An Experiment in Behavior Engineering
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Spatial Planning: A Configuration Space Approach
IEEE Transactions on Computers
Classifier fitness based on accuracy
Evolutionary Computation
An Introduction to Learning Fuzzy Classifier Systems
Learning Classifier Systems, From Foundations to Applications
A Bigger Learning Classifier Systems Bibliography
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
Fun2Mas: The Milan Robocup Team
RoboCup 2001: Robot Soccer World Cup V
QFCS: A Fuzzy LCS in Continuous Multi-step Environments with Continuous Vector Actions
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
To handle real valued input in XCS: using fuzzy hyper-trapezoidal membership in classifier condition
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
Towards final rule set reduction in XCS: a fuzzy representation approach
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Self organizing classifiers and niched fitness
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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We discuss some issues concerning the application of learning classifier systems to real-valued applications. In particular, we focus on the possibility of classifying data by crisp and fuzzy intervals, showing the effect of their granularity on the learning performance. We introduce the concept of sensorial cluster and we discuss the difference between cluster aliasing and perceptual aliasing. We show the impact of different choices on the performance of both crisp and fuzzy learning classifier systems applied to a mobile, autonomous, robotic agent.