Title :
Dynamic Logic learning in cognitive-based situation models
Author :
Ilin, Roman ; Perlovsky, Leonid
Author_Institution :
Sensors Directorate, Air Force Res. Lab., Hanscom AFB, MA, USA
Abstract :
We present a cognitive modeling framework called Neural Modeling Fields (NMF) and its application to situation learning and categorization. We discuss how this framework is related to the perceptual symbol systems theory of cognition (PSS). Essentially, the mathematical apparatus of NMF is a way to learn the frames and simulators described qualitatively by PSS. For the purposes of this work, a situation is modeled as a set of objects and relationships that exist among them. Here we consider object recognition problem solved and demonstrate how the NMF framework is used to learn high level concepts such as situations.
Keywords :
cognition; cognitive systems; formal logic; learning (artificial intelligence); neural nets; NMF framework; cognitive modeling; cognitive-based situation model; dynamic logic learning; neural modeling fields; object recognition problem; perceptual symbol systems theory; situation categorization; situation learning; Adaptation model; Cognition; Computational modeling; Data models; Mathematical model; Sensors; Testing; Dynamic Logic learning; Neural Modeling Fields; Perceptual symbol systems; Situation learning; Situation modeling;
Conference_Titel :
Cognitive Methods in Situation Awareness and Decision Support (CogSIMA), 2011 IEEE First International Multi-Disciplinary Conference on
Conference_Location :
Miami Beach, FL
Print_ISBN :
978-1-61284-785-6
DOI :
10.1109/COGSIMA.2011.5753751
