Modeling binding and cross-modal learning in Markov logic networks

doi:10.1016/j.neucom.2012.01.037

Modeling binding and cross-modal learning in Markov logic networks

2012-11-01

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DOI：10.1016/j.neucom.2012.01.037

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Binding - the ability to combine two or more modal representations of the same entity into a single shared representation - is vital for every cognitive system operating in a complex environment. In order to successfully adapt to changes in a dynamic environment the binding mechanism has to be supplemented with cross-modal learning. In this paper we define the problems of high-level binding and cross-modal learning. By these definitions we model a binding mechanism in a Markov logic network and define its role in a cognitive architecture. We evaluate a prototype binding system off-line, using three different inference methods. (C) 2012 Elsevier ay. All rights reserved.

Keywords:

Binding

Cross-modal learning

Graphical models

Markov logic networks

Cognitive systems

Modeling binding and cross-modal learning in Markov logic networks

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