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Journal of Artificial General Intelligence

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Conceptual Commitments of the LIDA Model of Cognition

Stan Franklin
  • Corresponding author
  • Fedex Institute of Technology 301, The University of Memphis, TN 38152, USA
  • Email:
/ Steve Strain
  • Corresponding author
  • Fedex Institute of Technology 301, The University of Memphis, TN 38152, USA
  • Email:
/ Ryan McCall
  • Corresponding author
  • Fedex Institute of Technology 301, The University of Memphis, TN 38152, USA
  • Email:
/ Bernard Baars
  • Corresponding author
  • 6615 Fisher Ave. Falls Church, VA 22046, USA
  • Email:
Published Online: 2014-04-25 | DOI: https://doi.org/10.2478/jagi-2013-0002

Abstract

Significant debate on fundamental issues remains in the subfields of cognitive science, including perception, memory, attention, action selection, learning, and others. Psychology, neuroscience, and artificial intelligence each contribute alternative and sometimes conflicting perspectives on the supervening problem of artificial general intelligence (AGI). Current efforts toward a broad-based, systems-level model of minds cannot await theoretical convergence in each of the relevant subfields. Such work therefore requires the formulation of tentative hypotheses, based on current knowledge, that serve to connect cognitive functions into a theoretical framework for the study of the mind. We term such hypotheses “conceptual commitments” and describe the hypotheses underlying one such model, the Learning Intelligent Distribution Agent (LIDA) Model. Our intention is to initiate a discussion among AGI researchers about which conceptual commitments are essential, or particularly useful, toward creating AGI agents.

Keywords : asynchrony; cognitive cycle; cognitive model; commitments; consciousness; embodied; Global Workspace Theory; learning; LIDA; memory; motivation; non-linear dynamics; theta-gamma coupling

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About the article

Received: 2013-04-09

Accepted: 2013-07-08

Published Online: 2014-04-25

Published in Print: 2013-06-01



Citation Information: Journal of Artificial General Intelligence, ISSN (Online) 1946-0163, DOI: https://doi.org/10.2478/jagi-2013-0002. Export Citation

© Stan Franklin et al.. This article is distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY-NC-ND 3.0)

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