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2001050713 contributor B8956442.
2001050713 created "c2001.".
2001050713 date "2001".
2001050713 date "c2001.".
2001050713 dateCopyrighted "c2001.".
2001050713 description "Includes bibliographical references (p. [165]-170) and index.".
2001050713 description "Machine generated contents note: 1. BACKGROUND -- 1. Anticipations -- 1.1. Psychology Discovers Anticipations -- 1.2. Theory of Anticipatory Behavioral Control -- 1.3. Importance of Anticipations -- 2. Genetic Algorithms -- 2.1. Evolutionary Principles -- 2.2. GA Framework -- 2.3. An Illustrative Example -- 3. Learning Classifier Systems -- 3.1. Holland's Cognitive System -- 3.2. LCS framework -- 3.3. Problems in Traditional LCSs -- 3.4. XCS Classifier System -- 2. ACS2 -- 1. Framework -- 1.1. Environmental Interaction -- 1.2. Knowledge Representation -- 1.3. A Behavioral Act -- 2. Reinforcement Learning -- 3. The Anticipatory Learning Process -- 3.1. The Process in Detail -- 3.2. The ALP in Action: A Simple Gripper Problem -- 3.3. Causes for Over-Specialization -- 4. Genetic Generalization in ACS2 -- 4.1. Accurate, Maximally General Classifiers in ACS2 -- 4.2. The GA Idea -- 4.3. How the GA Works -- 5. Interaction of ALP, GA, RL, and Behavior -- 5.1. Subsumption -- 5.2. Evolutionary Pressures of ALP and GA -- 5.3. All Interactions -- 3. EXPERIMENTS WITH ACS2 -- 1. Gripper Problem Revisited -- 1.1. Population without GA -- 1.2. Population with GA -- 2. Multiplexer Problem -- 2.1. Environmental Setting -- 2.2. Evolution of a Multiplexer Model -- 2.3. ACS2 as a Classifier -- 3. Maze Environment -- 3.1. Environmental Setting -- 3.2. Maze6 -- 3.3. Woodsl4 -- 4. Blocks World -- 4.1. Environmental Setting -- 4.2. Model Learning -- 5. Hand-Eye Coordination Task -- 5.1. Environmental Setting -- 5.2. Model Learning -- 6. Result Summary -- 4. LIMITS -- 1. GA Challenges -- 1.1. Overlapping Classifiers -- 1.2. Interfering Specificities -- 2. Non-determinism and a First Approach -- 2.1. ACS2 in a Non-determinism Task -- 2.2. Probability-Enhanced Effects -- 3. Model Aliasing -- 5. MODEL EXPLOITATION -- 1. Improving Model Learning -- 1.1. Increasing Exploration -- 1.2. Combining Exploration with Action Planning -- 2. Enhancing Reinforcement Learning -- 2.1. Response-Effect Learning Task -- 2.2. Mental Acting -- 2.3. Lookahead Action Selection -- 2.4. ACS2 in the Response-Effect Task -- 2.5. Stimulus-Response-Effect Task -- 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 -- 2.1. Classifier Structure -- 2.2. ACS2 Structure -- 3. Model Learning Modifications -- 3.1. Observations in Nature -- 3.2. Relevance and Influence -- 3.3. Attentional Mechanisms -- 3.4. Additional Memory -- 4. Adaptive Behavior -- 4.1. Reinforcement Learning Processes -- 4.2. Behavioral Module -- 5. ACS2 in the Future -- Appendices -- APPENDIX A: Parameters in ACS2 -- APPENDIX B: Algorithmic Description of ACS2 -- 1. Initialization -- 2. The Main Execution Loop -- 3. Formation of the Match Set -- 4. Choosing an Action -- 5. Formation of the Action Set -- 6. Application of the ALP -- 7. Reinforcement Learning -- 8. GA Application -- 9. Subsumption -- APPENDIX C: ACS2 C++ Code Documentation -- 1. Getting Started -- 2. Structure of the Code -- 2.1. The Controller - ACSConstants.h -- 2.2. The Executer - acs2++. cc -- 2.3. Environments -- 2.4. ACS2 modules -- 3. Performance Output -- APPENDIX D: Glossary.".
2001050713 extent "xxviii, 172 p. :".
2001050713 identifier "0792376307".
2001050713 identifier 2001050713-d.html.
2001050713 identifier 2001050713.html.
2001050713 isPartOf "Genetic algorithms and evolutionary computation ; 4".
2001050713 issued "2001".
2001050713 issued "c2001.".
2001050713 language "eng".
2001050713 publisher "Boston : Kluwer Academic Publishers,".
2001050713 subject "005.1 21".
2001050713 subject "Evolutionary computation.".
2001050713 subject "Evolutionary programming (Computer science)".
2001050713 subject "QA76.618 .B88 2001".
2001050713 subject "Self-organizing systems Data processing.".
2001050713 tableOfContents "Machine generated contents note: 1. BACKGROUND -- 1. Anticipations -- 1.1. Psychology Discovers Anticipations -- 1.2. Theory of Anticipatory Behavioral Control -- 1.3. Importance of Anticipations -- 2. Genetic Algorithms -- 2.1. Evolutionary Principles -- 2.2. GA Framework -- 2.3. An Illustrative Example -- 3. Learning Classifier Systems -- 3.1. Holland's Cognitive System -- 3.2. LCS framework -- 3.3. Problems in Traditional LCSs -- 3.4. XCS Classifier System -- 2. ACS2 -- 1. Framework -- 1.1. Environmental Interaction -- 1.2. Knowledge Representation -- 1.3. A Behavioral Act -- 2. Reinforcement Learning -- 3. The Anticipatory Learning Process -- 3.1. The Process in Detail -- 3.2. The ALP in Action: A Simple Gripper Problem -- 3.3. Causes for Over-Specialization -- 4. Genetic Generalization in ACS2 -- 4.1. Accurate, Maximally General Classifiers in ACS2 -- 4.2. The GA Idea -- 4.3. How the GA Works -- 5. Interaction of ALP, GA, RL, and Behavior -- 5.1. Subsumption -- 5.2. Evolutionary Pressures of ALP and GA -- 5.3. All Interactions -- 3. EXPERIMENTS WITH ACS2 -- 1. Gripper Problem Revisited -- 1.1. Population without GA -- 1.2. Population with GA -- 2. Multiplexer Problem -- 2.1. Environmental Setting -- 2.2. Evolution of a Multiplexer Model -- 2.3. ACS2 as a Classifier -- 3. Maze Environment -- 3.1. Environmental Setting -- 3.2. Maze6 -- 3.3. Woodsl4 -- 4. Blocks World -- 4.1. Environmental Setting -- 4.2. Model Learning -- 5. Hand-Eye Coordination Task -- 5.1. Environmental Setting -- 5.2. Model Learning -- 6. Result Summary -- 4. LIMITS -- 1. GA Challenges -- 1.1. Overlapping Classifiers -- 1.2. Interfering Specificities -- 2. Non-determinism and a First Approach -- 2.1. ACS2 in a Non-determinism Task -- 2.2. Probability-Enhanced Effects -- 3. Model Aliasing -- 5. MODEL EXPLOITATION -- 1. Improving Model Learning -- 1.1. Increasing Exploration -- 1.2. Combining Exploration with Action Planning -- 2. Enhancing Reinforcement Learning -- 2.1. Response-Effect Learning Task -- 2.2. Mental Acting -- 2.3. Lookahead Action Selection -- 2.4. ACS2 in the Response-Effect Task -- 2.5. Stimulus-Response-Effect Task -- 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 -- 2.1. Classifier Structure -- 2.2. ACS2 Structure -- 3. Model Learning Modifications -- 3.1. Observations in Nature -- 3.2. Relevance and Influence -- 3.3. Attentional Mechanisms -- 3.4. Additional Memory -- 4. Adaptive Behavior -- 4.1. Reinforcement Learning Processes -- 4.2. Behavioral Module -- 5. ACS2 in the Future -- Appendices -- APPENDIX A: Parameters in ACS2 -- APPENDIX B: Algorithmic Description of ACS2 -- 1. Initialization -- 2. The Main Execution Loop -- 3. Formation of the Match Set -- 4. Choosing an Action -- 5. Formation of the Action Set -- 6. Application of the ALP -- 7. Reinforcement Learning -- 8. GA Application -- 9. Subsumption -- APPENDIX C: ACS2 C++ Code Documentation -- 1. Getting Started -- 2. Structure of the Code -- 2.1. The Controller - ACSConstants.h -- 2.2. The Executer - acs2++. cc -- 2.3. Environments -- 2.4. ACS2 modules -- 3. Performance Output -- APPENDIX D: Glossary.".
2001050713 title "Anticipatory learning classifier systems / by Martin V. Butz.".
2001050713 type "text".