Assessing the Role of Metacognition in GMU BICA

Authors:
Michael Q. Kalish;Alexei V. Samsonovich;Mark A. Coletti;Kenneth A. De Jong
Affiliations:
Arts and Sciences Undergraduate School, University of Virginia, P.O. Box 400133, Garrett Hall, Charlottesville, VA 22904, USA, kalishmichael@gmail.com;Krasnow Institute for Advanced Study, George Mason University, 4400 University Drive, Fairfax, VA 22030, USA, asamsono@gmu.edu,;Krasnow Institute for Advanced Study, George Mason University, 4400 University Drive, Fairfax, VA 22030, USA, asamsono@gmu.edu, and Computer Science Department, George Mason University, 4400 Unive ...;Krasnow Institute for Advanced Study, George Mason University, 4400 University Drive, Fairfax, VA 22030, USA, asamsono@gmu.edu, and Computer Science Department, George Mason University, 4400 Unive ...
Venue:
Proceedings of the 2010 conference on Biologically Inspired Cognitive Architectures 2010: Proceedings of the First Annual Meeting of the BICA Society
Year:
2010

Citing 4
Cited 1

SOAR: an architecture for general intelligence

Artificial Intelligence
Planning Algorithms

Planning Algorithms
Extending the Soar Cognitive Architecture

Proceedings of the 2008 conference on Artificial General Intelligence 2008: Proceedings of the First AGI Conference
Engineering intelligent information-processing systems with CAST

Advanced Engineering Informatics

Toward a Unified Catalog of Implemented Cognitive Architectures

Proceedings of the 2010 conference on Biologically Inspired Cognitive Architectures 2010: Proceedings of the First Annual Meeting of the BICA Society

Quantified Score

Hi-index	0.00

Visualization

Abstract

GMU BICA is a biologically inspired cognitive architecture developed at George Mason University. Its main distinguishing feature is a system of data structures called “mental states” that enables various forms of metacognition. The present study develops an understanding of the role of metacognition during working scenario generation (a general element of the cognitive cycle in GMU BICA). This is done by a computational experiment with a rapid prototype of the architecture that generates metacognitive mental states. A spatial learning / spatial navigation paradigm is used here, with a virtual robot implemented in CASTLE. Results show a significant increase in both behavioral and cognitive efficiency of the agent when metacognition is turned on. Generalizations are discussed.