Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Signals & systems (2nd ed.)
Learning how to combine sensory-motor functions into a robust behavior
Artificial Intelligence
Understanding human intentions via hidden markov models in autonomous mobile robots
Proceedings of the 3rd ACM/IEEE international conference on Human robot interaction
International Journal of Robotics Research
Behavior categorization using Correlation Based Adaptive Resonance Theory
MED '09 Proceedings of the 2009 17th Mediterranean Conference on Control and Automation
CobART: Correlation Based Adaptive Resonance Theory
MED '09 Proceedings of the 2009 17th Mediterranean Conference on Control and Automation
International Journal of Robotics Research
Robot introspection through learned hidden Markov models
Artificial Intelligence
Incremental clustering of gesture patterns based on a self organizing incremental neural network
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Computational intelligence for structured learning of a partner robot based on imitation
Information Sciences: an International Journal
Learning the behavior model of a robot
Autonomous Robots
Codevelopmental Learning Between Human and Humanoid Robot Using a Dynamic Neural-Network Model
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
IEEE Transactions on Information Theory
The evolution of imitation and mirror neurons in adaptive agents
Cognitive Systems Research
On the dynamics of robot exploration learning
Cognitive Systems Research
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This paper proposes behavior learning and generation models for simple and complex behaviors of robots using unsupervised learning methods. While the simple behaviors are modeled by simple-behavior learning model (SBLM), complex behaviors are modeled by complex-behavior learning model (CBLM) which uses previously learned simple or complex behaviors. Both models include behavior categorization, behavior modeling, and behavior generation phases. In the behavior categorization phase, sensory data are categorized using correlation based adaptive resonance theory (CobART) network that generates motion primitives corresponding to robot's base abilities. In the behavior modeling phase, a modified version of hidden Markov model (HMM), is called Behavior-HMM, is used to model the relationships among the motion primitives in a finite state stochastic network. At the same time, a motion generator which is an artificial neural network (ANN) is trained for each motion primitive to learn essential robot motor commands. In the behavior generation phase, a motion primitive sequence that can perform the desired task is generated according to the previously learned Behavior-HMMs at the higher level. Then, in the lower level, these motion primitives are executed by the motion generator which is specifically trained for the corresponding motion primitive. The transitions between the motion primitives are done according to observed sensory data and probabilistic weights assigned to each transition during the learning phase. The proposed models are not constructed for one specific behavior, but are intended to be bases for all behaviors. The behavior learning capabilities of the model is extended by integrating previously learned behaviors hierarchically which is referred as CBLM. Hence, new behaviors can take advantage of already discovered behaviors. Performed experiments on a robot simulator show that simple and complex-behavior learning models can generate requested behaviors effectively.