Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Classifier systems and genetic algorithms
Artificial Intelligence
A critical review of classifier systems
Proceedings of the third international conference on Genetic algorithms
Made-up minds: a constructivist approach to artificial intelligence
Made-up minds: a constructivist approach to artificial intelligence
Concerning the emergence of tag-mediated lookahead in classifier systems
Emergent computation
Reinforcement learning architectures for animats
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
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
Learning to Perceive and Act by Trial and Error
Machine Learning
Technical Note: \cal Q-Learning
Machine Learning
Adding temporary memory to ZCS
Adaptive Behavior
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Robot Shaping: An Experiment in Behavior Engineering
Robot Shaping: An Experiment in Behavior Engineering
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Symbiogenesis in learning clasifier systems
Artificial Life
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
Properties of the Bucket Brigade
Proceedings of the 1st International Conference on Genetic Algorithms
Investigating Generalization in the Anticipatory Classifier System
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Toward Optimal Classifier System Performance in Non-Markov Environments
Evolutionary Computation
Classifier fitness based on accuracy
Evolutionary Computation
An analysis of generalization in the xcs classifier system
Evolutionary Computation
Automated global structure extraction for effective local building block processing in XCS
Evolutionary Computation
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Learning Mazes with Aliasing States: An LCS Algorithm with Associative Perception
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Anticipatory Learning Classifier Systems and Factored Reinforcement Learning
Anticipatory Behavior in Adaptive Learning Systems
Considering Unseen States as Impossible in Factored Reinforcement Learning
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Anticipation behavior implemented in a simulator of artificial life
MIC '08 Proceedings of the 27th IASTED International Conference on Modelling, Identification and Control
Building robots with analogy-based anticipation
KI'06 Proceedings of the 29th annual German conference on Artificial intelligence
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
An evolutionary behavioral model for decision making
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
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The anticipatory classifier system (ACS)combines the learning classifier system frameworkwith the cognitive learning theory ofanticipatory behavioral control. The result is an evolutionary system thatbuilds a complete and generalized predictiveenvironmental model. Reinforcement learningtechniques are applied to form a behavioralpolicy represented in the model. After providingsome background as well as outlining the objectives of the system, we explainin detail all involved current processes. Furthermore, we analyze thedeficiency of over-specialization in the anticipatory learning process (ALP),the main learning mechanism in the ACS. Consequently, we introduce a geneticalgorithm (GA) to the ACS that is meant for generalization of over-specializedclassifiers. We show that it is possible to form a symbiosis between a directedspecialization and a genetic generalization mechanism achieving a learningmechanism that evolves a complete, accurate, and compact description of theperceived environment. Results in three different environmental settingsconfirm the usefulness of the genetic algorithm in the ACS. Finally, we discuss future research directions.