Introduction to the theory of neural computation
Introduction to the theory of neural computation
Motor primitive and sequence self-organization in a hierarchical recurrent neural network
Neural Networks - 2004 Special issue: New developments in self-organizing systems
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
Pure reactive behavior learning using Case Based Reasoning for a vision based 4-legged robot
Robotics and Autonomous Systems
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
Robot introspection through learned hidden Markov models
Artificial Intelligence
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
Humanoid robot behavior learning based on ART neural network and cross-modality learning
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
The evolution of imitation and mirror neurons in adaptive agents
Cognitive Systems Research
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This paper introduces a new model for robot behavior categorization. Correlation based adaptive resonance theory (CobART) networks are integrated hierarchically in order to develop an adequate categorization, and to elicit various behaviors performed by the robot. The proposed model is developed by adding a second layer CobART network which receives first layer CobART network categories as an input, and back-propagates the matching information to the first layer networks. The first layer CobART networks categorize self-behavior data of a robot or an object in the environment while the second layer CobART network categorizes the robot's behavior with respect to its effect on the object. Experiments show that the proposed model generates reasonable categorization of behaviors being tested. Moreover, it can learn different forms of the behaviors, and it can detect the relations between them. In essence, the model has an expandable architecture and it contains reusable parts. The first layer CobART networks can be integrated with other CobART networks for another categorization task. Hence, the model presents a way to reveal all behaviors performed by the robot at the same time.