Genetic algorithms and evolutionary computation, 4.
CONTENTS NOTE
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1. Background --;1. Anticipations --;2. Genetic Algorithms --;3. Learning Classifier Systems --;2. ACS2 --;1. Framework --;2. Reinforcement Learning --;3. The Anticipatory Learning Process --;4. Genetic Generalization in ACS2 --;5. Interaction of ALP, GA, RL, and Behavior --;3. Experiments with ACS2 --;1. Gripper Problem Revisited --;2. Multiplexer Problem --;3. Maze Environment --;4. Blocks World --;5. Hand-Eye Coordination Task --;6. Result Summary --;4. Limits --;1. GA Challenges --;2. Non-determinism and a First Approach --;3. Model Aliasing --;5. Model Exploitation --;1. Improving Model Learning --;2. Enhancing Reinforcement Learning --;3. Model Exploitation Recapitulation --;6. Related Systems --;1. Estimated Learning Algorithm --;2. Dyna --;3. Schema Mechanism --;4. Expectancy Model SRS/E --;7. Summary, Conclusions, and Future Work --;1. Summary --;2. Model Representation Enhancements --;3. Model Learning Modifications --;4. Adaptive Behavior --;5. ACS2 in the Future --;Appendices --;References.
SUMMARY OR ABSTRACT
Text of Note
Anticipatory Learning Classifier Systems describes the state of the art of anticipatory learning classifier systems-adaptive rule learning systems that autonomously build anticipatory environmental models.