Concerning the emergence of tag-mediated lookahead in classifier systems
CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
Made-up minds: a constructivist approach to artificial intelligence
Made-up minds: a constructivist approach to artificial intelligence
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
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
Learning in embedded systems
Mechanism and process in animal behavior: models of animals, animals as models
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
A hierarchical classifier system implementing a motivationally autonomous animat
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning
Artificial Intelligence
Robot Shaping: An Experiment in Behavior Engineering
Robot Shaping: An Experiment in Behavior Engineering
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Learning Classifier Systems, From Foundations to Applications
Learning Classifier Systems, From Foundations to Applications
Classifier fitness based on accuracy
Evolutionary Computation
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Model-based learning for mobile robot navigation from the dynamicalsystems perspective
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Anticipations, Brains, Individual and Social Behavior: An Introduction to Anticipatory Systems
Anticipatory Behavior in Adaptive Learning Systems
Anticipatory Behavior in Adaptive Learning Systems
Learning Classifier Systems: Looking Back and Glimpsing Ahead
Learning Classifier Systems
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Mind as an anticipatory device: for a theory of expectations
BVAI'05 Proceedings of the First international conference on Brain, Vision, and Artificial Intelligence
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The concept of anticipations controlling behavior is introduced. Background is provided about the importance of anticipations from a psychological perspective. Based on the psychological background wrapped in a framework of anticipatory behavioral control, the anticipatory learning classifier system ACS2 is explained. ACS2 learns and generalizes on-line a predictive environmental model (a model that allows the prediction of future environmental states). The model is a subjective model, that is, no global state information is available to the agent. It is shown that ACS2 can simulate anticipatory learning processes and anticipatory controlled behavior by means of the model. The simulations of various rat experiments, previously conducted by Colwill and Rescoria, show that the incorporation of anticipations is indeed crucial for simulating the behavior observed in rats. Despite the simplicity of the tasks, we show that the observed behavior reaches beyond the capabilities of model-free reinforcement learning as well as model-based reinforcement learning without on-line generalization. Possible future impacts of anticipations in adaptive learning systems are outlined.