Proceedings of the seventh international conference (1990) on Machine learning
Learning in embedded systems
Adaptation in dynamic environments through a minimal probability of exploration
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
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Machine Learning
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Introduction to Reinforcement Learning
An Introduction to Anticipatory Classifier Systems
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Latent Learning and Action Planning in Robots with Anticipatory Classifier Systems
Learning Classifier Systems, From Foundations to Applications
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Probability-Enhanced Predictions in the Anticipatory Classifier System
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
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IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
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Evolutionary Computation
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Evolutionary Computation
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Journal of Artificial Evolution and Applications
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IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
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GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
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Fundamenta Informaticae
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GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
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Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
A Recursive Classifier System for Partially Observable Environments
Fundamenta Informaticae
A New Architecture for Learning Classifier Systems to Solve POMDP Problems
Fundamenta Informaticae
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The chapter investigates how model and behavioral learning can be improved in an anticipatory learning classifier system by biasing exploration. First, the applied system ACS2 is explained. Next, an overview over the possibilities of applying exploration biases in an anticipatory learning classifier system and specifically ACS2 is provided. In ACS2, a recency bias termed action delay bias as well as an error bias termed knowledge array bias is implemented. The system is applied in a dynamic maze task and an hand-eye coordination task to validate the biases. The experiments exhibit that biased exploration enables ACS2 to evolve and adapt its internal environmental model faster. Also adaptive behavior is improved.