Hierarchical behavior categorization using correlation based adaptive resonance theory

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...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) 2012-02, Vol.77 (1), p.71-81
Main Authors: Yavaş, Mustafa, Alpaslan, Ferda Nur
Format: Article
Language:English
Subjects:
Adaptive resonance theory
Architecture
Categories
Correlation
Machine learning
Matching
Networks
Reusable
Robot behavior categorization
Robots
Tasks
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Description
Summary: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.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2011.08.022