Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Computational models of classical conditioning: a comparative study
Proceedings of the fifth international conference on simulation of adaptive behavior on From animals to animats 5
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Qualitative Analysis of Behavior, Vol. 11: Neural Network Models of Conditioning and Action
Qualitative Analysis of Behavior, Vol. 11: Neural Network Models of Conditioning and Action
Representation and timing in theories of the dopamine system
Neural Computation
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In order to adapt the behavior of robots to varying environments, conditioning models provide interesting ideas. A prediction system is an important part of such models. The problem is to update it according to the sequence of stimuli perceived by the robot. Bayesian networks can be used to implement the prediction system. However, update rules are very complex and we need an incremental and fast learning process. We propose the use of noisy or nodes with appropriate learning rules. Numerous features of conditioning have been tested and promising results have been obtained.