Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Journal of Artificial General Intelligence

The Journal of the Artificial General Intelligence Society

3 Issues per year

Open Access
See all formats and pricing
More options …

Cognitive Architectures and Autonomy: A Comparative Review

Kristinn Thórisson
  • Center for Analysis & Design of Intelligent Agents, School of Computer Science Venus, 2nd fl. Reykjavik University Menntavegur 1, 101 Reykjavik, Iceland
  • Icelandic Institute for Intelligent Machines, 2. h. Uranus Menntavegur 1, 101 Reykjavik, Iceland
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Helgi Helgasson
  • Center for Analysis & Design of Intelligent Agents, School of Computer Science Venus, 2nd fl. Reykjavik University Menntavegur 1, 101 Reykjavik, Iceland
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2012-05-21 | DOI: https://doi.org/10.2478/v10229-011-0015-3


One of the original goals of artificial intelligence (AI) research was to create machines with very general cognitive capabilities and a relatively high level of autonomy. It has taken the field longer than many had expected to achieve even a fraction of this goal; the community has focused on building specific, targeted cognitive processes in isolation, and as of yet no system exists that integrates a broad range of capabilities or presents a general solution to autonomous acquisition of a large set of skills. Among the reasons for this are the highly limited machine learning and adaptation techniques available, and the inherent complexity of integrating numerous cognitive and learning capabilities in a coherent architecture. In this paper we review selected systems and architectures built expressly to address integrated skills. We highlight principles and features of these systems that seem promising for creating generally intelligent systems with some level of autonomy, and discuss them in the context of the development of future cognitive architectures. Autonomy is a key property for any system to be considered generally intelligent, in our view; we use this concept as an organizing principle for comparing the reviewed systems. Features that remain largely unaddressed in present research, but seem nevertheless necessary for such efforts to succeed, are also discussed.

Keywords: cognitive architectures; autonomy; constructivist AI; realtime; meta-learning

  • Anderson, J. R. 1996. ACT: A simple theory of complex cognition. American Psychologist. 51: 355-365.CrossrefGoogle Scholar

  • Anderson, J. R., Matessa, M., Lebiere, C. 1997. ACT-R: A theory of higher level cognition and its relation to visual attention. Human-Computer Interaction. 12: 439-462.Google Scholar

  • Anderson, J. R., Lebiere, C. 2003. The Newell test for a theory of cognition. Behavioral and brain Sciences. 26: 587-601.Google Scholar

  • Anderson, J. R., John, B. E., Just, M. A., Carpenter, P. A., Kieras, D. E., & Meyer, D. E. (1995). Production system models of complex cognition. 17th Annual Meeting of the Cognitive Science Society, 9-12.Google Scholar

  • Baars, B. J. 1988. A Cognitive Theory of Consciousness. Cambridge, UK: Cambridge University Press.Google Scholar

  • Baars, B. J., Franklin, S. 2009. Consciousness is computational: The LIDA model of Global Workspace Theory. International Journal of Machine Consciousness, 2009. 1(1): p. 23-32.Google Scholar

  • Brooks, R. A. 1991. Intelligence without representation. Artificial Intelligence. 47 (1-3): 139-159.Google Scholar

  • Joanna J. Bryson (2003). Behavior-Oriented Design of Modular Agent Intelligence. In R. Kowalszyk, J. P. Müller, H. Tianfield and R. Unland (eds.), Agent Technologies, Infrastructures, Tools, and Applications for e-Services, pp. 61-76.Google Scholar

  • Dean, T., and Boddy, M. 1988. An analysis of time- dependent planning. In Proceedings of the Seventh National Conference on Artificial Intelligence, 49-54. Saint Paul, MN. AAAI, AAAI Press/The MIT Press.Google Scholar

  • Franklin, S. 2006. The LIDA architecture: Adding new modes of learning to an intelligent, autonomous software agent. In Proceedings of the International Conference on Integrated Design and Process Technology, PAGE NUMBERS: San Diego, CA. Society for Design and Process Science.Google Scholar

  • Garlan, D. & J. Ockerbloom 1995. Architectural Mismatch or Why it's Hard to Build Systems out of Existing Parts. Proceedings of the Seventh International Conference on Software Engineering, Seattle WA, AprilGoogle Scholar

  • Hall, J. S. 2008. VARIAC: An Autogenous Cognitive Architecture. In Proceedings of First Conference of Artificial General Intelligence, 176-187. Memphis, Tenn.: ISO Press.Google Scholar

  • Huang, H., Messina, E., Albus, J. 2003. Towards a Generic Model for Autonomy Levels for Unmanned Systems (ALFUS). In Proceedings of the 2003 PerMIS Workshop, Gaithersburg, MD: National Institute of Standards and Technology, Intelligent Systems Division.Google Scholar

  • Huang, H., Pavek, K., Novak, B., Albus, J., Messina, E. 2005. A Framework For Autonomy Levels For Unmanned Systems (ALFUS). In Proceedings of the Association for Unmanned Vehicle Systems International Unmanned Systems North America 2005, p. 849-863. Baltimore, MD: NIST.Google Scholar

  • Huang, H., Messina, E., Albus, J. 2004. Autonomy Levels for Unmanned Systems (ALFUS) Framework, Volume II: Framework Models Version 1.0. NIST Special Publication 1011-II-1.0, 2004.Google Scholar

  • Johnston, B. 2010. The Toy Box Problem (and a Preliminary Solution). In Proceedings of the Third Conference on Artificial General Intelligence, p. 43-48. Lugano, Switzerland: Springer.Google Scholar

  • Jonsdottir, G. R., Thórisson, K. R, Nivel, E. 2008. Learning Smooth, Human-Like Turntaking in Realtime Dialogue. In Proceedings of Intelligent Virtual Agents (IVA), p. 162-175. Tokyo, Japan: Springer.Google Scholar

  • Kurzweil, R. 2006. The Singularity is Near: When Humans Transcend Biology New York, NY: Viking Press.Google Scholar

  • Laird, J. E. (2008). Extending the Soar Cognitive Architecture. In Proceedings of the First Conference on Artificial General Intelligence, p. 224-235. Memphis, Tenn.: Springer.Google Scholar

  • Langley, P. 2005. An Adaptive Architecture for Physical Agents. In Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology, 18-25. Compiegne, France: Springer.Google Scholar

  • Langley, P., Laird, J. E., Rogers, S. 2009. Cognitive Architectures: Research Issues and Challenges. Cognitive Systems Research, 10(2), 141-160.Google Scholar

  • Ng-Thow-Hing, V., K. R. Thórisson, R. K. Sarvadevabhatla, J. Wormer & T. List. 2009. Cognitive Map Architecture: Facilitation of Human-Robot Interaction in Humanoid Robots. IEEE Robotics & Automation Magazine. 16(1): 55-66.Web of ScienceGoogle Scholar

  • Nivel, E. 2007. Ikon Flux 2.0., Technical Report, RUTR - CS07006, School of Computer Science, Reykjavik Univer.Google Scholar

  • Pezzulo, G., Calvi, G. 2006. A Schema Based Model of the Praying Mantis. In From Animals to Animats 9. In Proceedings of the Ninth International Conference on Simulation of Adaptive Behavior, 211-223. Rome, Italy: Springer.Google Scholar

  • Pezzulo, G., Calvi, G. 2007. Designing Modular Architectures in the Framework AKIRA. Multiagent and Grid Systems. 3: 65-86.Google Scholar

  • Pezzulo, G. 2009. DiPRA: A Layered Agent Architecture which Integrates Practical Reasoning and Sensorimotor Schemas. Connect Science. 21: 297-326.Web of ScienceGoogle Scholar

  • Pollock, J. L. 2008. OSCAR: An Architecture for Generally Intelligent Agents. Frontiers in Artificial Intelligence and Applications, p. 275-286.Google Scholar

  • Rao, A. S. and M. P. Georgeff. 1995. BDI-agents: From Theory to Practice. Proceedings of the First International Conference on Multiagent Systems (ICMAS'95), 312-319.Google Scholar

  • Roy, D. 2005. Semiotic Schemas: A Framework for Grounding Language in the Action and Perception. Artificial Intelligence, 167(1-2): 170-205.Google Scholar

  • Samsonovich, A. V. 2010. Toward a Unified Catalog of Implemented Cognitive Architectures. In A. V. Samsonovich, K. R. Jóhannsdóttir, A. Chella & B. Goertzel (Eds.), Proceeding of the 2010 Conference on Biologically Inspired Cognitive Architectures (pp. 195-244). Amsterdam: IOS Press.Google Scholar

  • Snaider, J., McCall, R., Franklin S. 2011. The LIDA framework as a general tool for AGI. Proceedings of the 2011 Conference on Artificial General Intelligence, p. 133-142.Google Scholar

  • Sun, R. 2006. The CLARION cognitive architecture: Extending cognitive modeling to social simulation. In: Ron Sun (ed.), Cognition and Multi-Agent Interaction. Cambridge University Press, New York.Google Scholar

  • Sun, R., Merrill, E., Peterson T. 2001. From implicit skills to explicit knowledge: A bottom-up model of skill learning. Cognitive Science. 25: 203-244.Google Scholar

  • Sun, R. 2003. A Detailed Specification of CLARION 5.0. Technical report. 2003.Google Scholar

  • Thórisson, K. R. 1997. Gandalf: An Embodied Humanoid Capable of Real-Time Multimodal Dialogue. In Proceedings of the People's First ACM International Conference on Autonomous Agents, 536-537. Marina del Rey, Calif.: ACM International Conference on Autonomous Agents.Google Scholar

  • Thórisson, K. R. 1999. A Mind Model for Multimodal Communicative Creatures and Humanoids. International Journal of Applied Artificial Intelligence. 13:(4-5): 519-538.Google Scholar

  • Thórisson, K. R., Benko, H., Abramov D., Arnold, A., Maskey, S., Vaseekaran A. 2004. Constructionist Design Methodology for Interactive Intelligences. AI Magazine. 25(4): 77-90.Google Scholar

  • Thórisson, K. R. 2009. From Constructionist to Constructivist A. I. Keynote, Technical Report, FS-09-01, AAAI press, Menlo Park, Calif.Google Scholar

  • Wang, P. 1995. Non-Axiomatic Reasoning System: Exploring the Essence of Intelligence. Ph.D. diss., Dept. of Computer Science, Indiana Univ., CITY, Indiana.Google Scholar

  • Wang, P. 1996. Problem-solving under insufficient resources. In Working Notes of the Symposium on Flexible Computation, 148-155. Cambridge, Mass.: AAAI Press.Google Scholar

  • Wang, P. 2006. Rigid Flexibility: The Logic of Intelligence. New York, NY: Springer.Google Scholar

About the article

Published Online: 2012-05-21

Citation Information: Journal of Artificial General Intelligence, Volume 3, Issue 2, Pages 1–30, ISSN (Online) 1946-0163, DOI: https://doi.org/10.2478/v10229-011-0015-3.

Export Citation

This content is open access.

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

Janusz A. Starzyk, James Graham, and Leszek Puzio
IEEE Transactions on Neural Networks and Learning Systems, 2017, Volume 28, Number 11, Page 2528
Janusz A. Starzyk and James Graham
IEEE Systems Journal, 2017, Volume 11, Number 3, Page 1272
Antonio Lieto, Mehul Bhatt, Alessandro Oltramari, and David Vernon
Cognitive Systems Research, 2017
Antonio Lieto, Christian Lebiere, and Alessandro Oltramari
Cognitive Systems Research, 2017
Ryutaro Ichise
Procedia Computer Science, 2016, Volume 88, Page 239

Comments (0)

Please log in or register to comment.
Log in