Language and cognition integration through modeling field theory: category formation for symbol grounding

Authors:
Vadim Tikhanoff;José F. Fontanari;Angelo Cangelosi;Leonid I. Perlovsky
Affiliations:
Adaptive Behaviour & Cognition, University of Plymouth, Plymouth, UK;Instituto de Física de São Carlos, Universidade de São Paulo, São Carlos, SP, Brazil;Adaptive Behaviour & Cognition, University of Plymouth, Plymouth, UK;Air Force Research Laboratory, Hanscom Air Force Base, MA
Venue:
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Year:
2006

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Abstract

Neural Modeling Field Theory is based on the principle of associating lower-level signals (e.g., inputs, bottom-up signals) with higher-level concept-models (e.g. internal representations, categories/concepts, top-down signals) avoiding the combinatorial complexity inherent to such a task. In this paper we present an extension of the Modeling Field Theory neural network for the classification of objects. Simulations show that (i) the system is able to dynamically adapt when an additional feature is introduced during learning, (ii) that this algorithm can be applied to the classification of action patterns in the context of cognitive robotics and (iii) that it is able to classify multi-feature objects from complex stimulus set. The use of Modeling Field Theory for studying the integration of language and cognition in robots is discussed.