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Induction: processes of inference, learning, and discovery
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Multiple paired forward and inverse models for motor control
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Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
2005 Special Issue: A model of evolution and learning
Neural Networks - 2005 Special issue: IJCNN 2005
MOSAIC Model for Sensorimotor Learning and Control
Neural Computation
Model-based learning for mobile robot navigation from the dynamicalsystems perspective
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Anticipatory Behavior in Adaptive Learning Systems
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The paper proposes the framework for an animat control system (the Animat Brain) that is based on the Petr K. Anokhin's theory of functional systems. We propose the animat control system that consists of a set of functional systems (FSs) and enables predictive and purposeful behavior. Each FS consists of two neural networks: the actor and the predictor. The actors are intended to form chains of actions and the predictors are intended to make prognoses of future events. There are primary and secondary repertoires of behavior: the primary repertoire is formed by evolution; the secondary repertoire is formed by means of learning. This paper describes both principles of the Animat Brain operation and the particular model of predictive behavior in a cellular landmark environment.