Behavior categorization using Correlation Based Adaptive Resonance Theory

Authors:
Mustafa Yavas;Ferda Nur Alpaslan
Affiliations:
Department of Computer Engineering, Middle East Technical University, Ankara, Turkey;Department of Computer Engineering, Middle East Technical University, Ankara, Turkey
Venue:
MED '09 Proceedings of the 2009 17th Mediterranean Conference on Control and Automation
Year:
2009

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Hierarchical behavior categorization using correlation based adaptive resonance theory

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Simple and complex behavior learning using behavior hidden Markov model and CobART

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Abstract

This paper presents a new method of categorizing robot behavior, which is based on a variation of Correlation Based Adaptive Resonance Theory (CobART) learning. CobART is a type of ART 2 network and its main contribution is the usage of correlation analysis methods for category matching. This study uses derivation based correspondence and Euclidian distance as correlation analysis methods for behavior categorization. Tests show that the proposed method generates better results than ART 2 categorization even when a priori SOM (Self-Organizing Map) categorization is combined with ART 2 categorization.