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

fYear

2011

fDate

22-24 Feb. 2011

Firstpage

32

Lastpage

39

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/COGSIMA.2011.5753751

Filename

5753751