The Action Execution Process Implemented in Different Cognitive Architectures: A Review

Open access

Abstract

An agent achieves its goals by interacting with its environment, cyclically choosing and executing suitable actions. An action execution process is a reasonable and critical part of an entire cognitive architecture, because the process of generating executable motor commands is not only driven by low-level environmental information, but is also initiated and affected by the agent’s high-level mental processes. This review focuses on cognitive models of action, or more specifically, of the action execution process, as implemented in a set of popular cognitive architectures. We examine the representations and procedures inside the action execution process, as well as the cooperation between action execution and other high-level cognitive modules. We finally conclude with some general observations regarding the nature of action execution.

References

  • ACT-R 6.0 Tutorial. 2012. Unpublished manuscript. Retrieved from http://actr.psy.cmu.edu/wordpress/wp-content/themes/ACT-R/actr6/actr6.zip

  • Albus, J. S., and Barbera, A. J. 2005. RCS: A cognitive architecture for intelligent multi-agent systems. Annual Reviews in Control, 29(1):87-99.

  • Anderson, J. 2007. How can the human mind occur in the physical universe? : Oxford University Press.

  • Anderson, J. R., Bothell, D., Byrne, M. D., Douglass, S., Lebiere, C., and Qin, Y. 2004. An integrated theory of the mind. Psychological Review, 111(4):1036-1060.

  • Arrabales, R., Ledezma, A., and Sanchis, A. 2009. CERA-CRANIUM: A test bed for machine consciousness research. International Workshop on Machine Consciousness.

  • Baars, B. J. 1988. A cognitive theory of consciousness. New York: Cambridge University Press.

  • Baars, B. J. 2002. The conscious access hypothesis: origins and recent evidence. Trends in cognitive sciences, 6(1):47-52.

  • Bothell, D. n.d. ACT-R 6.0 Reference Manual (Working Draft). Retrieved from http://actr.psy.cmu.edu/wordpress/wp-content/themes/ACT-R/actr6/reference-manual.pdf

  • Brooks, R. A. 1991. How to build complete creatures rather than isolated cognitive simulators.Architectures for Intelligence: The Twenty-second Carnegie Mellon Symposium on Cognition:225-239.

  • Budiu, R. 2013. ACT-R Website. from http://act-r.psy.cmu.edu/

  • Byrne, M. D., and Anderson, J. R. 2001. Serial modules in parallel: The psychological refractory period and perfect time-sharing. Psychological Review, 108(4):847-869.

  • Castiello, U. 2005. The neuroscience of grasping. Nature Reviews Neuroscience, 6(9):726-736.

  • Dickmanns, E. D. 1992. A general dynamic vision architecture for UGV and UAV. Applied Intelligence, 2(3):251-270.

  • Dickmanns, E. D. 2000. An expectation-based, multi-focal, saccadic (EMS) vision system for vehicle guidance. Paper presented at the International Symposium of Robotics Research (ISRR’99), 421-430, Snowbird Utah, USA.

  • Dong, D., and Franklin, S. 2014. Sensory Motor System: Modeling the process of action execution. Paper presented at the Proceedings of the 36th Annual Conference of the Cognitive Science Society, 2145-2150, Austin TX, USA.

  • Duch, W., Oentaryo, R. J., and Pasquier, M. 2008. Cognitive Architectures: Where do we go from here? Paper presented at the AGI, 122-136, Memphis TN, USA.

  • Franklin, S., and Graesser, A. 1997. Is it an Agent, or just a Program?: A Taxonomy for Autonomous Agents Intelligent agents III agent theories, architectures, and languages, 21-35.London, UK: Springer-Verlag.

  • Franklin, S., Madl, T., D’Mello, S., and Snaider, J. 2014. LIDA: A Systems-level Architecture for Cognition, Emotion, and Learning. IEEE Transactions on Autonomous Mental Development, 6(1):19-41. doi: 10.1109/TAMD.2013.2277589

  • Goertzel, B., Lian, R., Arel, I., De Garis, H., and Chen, S. 2010. A world survey of artificial brain projects, Part II: Biologically inspired cognitive architectures. Neurocomputing, 74(1):30-49.

  • Goodale, M. A., and Milner, A. D. 1992. Separate visual pathways for perception and action.Trends in neurosciences, 15(1):20-25.

  • Grafton, S. T. 2010. The cognitive neuroscience of prehension: recent developments. Experimental brain research, 204(4):475-491. Jeannerod, M. 2006. Motor cognition: What actions tell the self. Oxford, UK: Oxford University Press.

  • Kieras, D. E., and Meyer, D. E. 1996. The EPIC architecture: Principles of operation. Unpublished manuscript from ftp://ftp.eecs.umich.edu/people/kieras/EPICarch.ps.

  • Kieras, D. E., and Meyer, D. E. 1997. An overview of the EPIC architecture for cognition and performance with application to human-computer interaction. Human-computer interaction, 12(4):391-438.

  • Laird, J. 2012. The Soar cognitive architecture: MIT Press.

  • Laird, J. E. 2008. Extending the Soar cognitive architecture. Paper presented at the Artificial General Intelligence 2008, 224-235, Memphis TN, USA.

  • Laird, J. E., Congdon, C. B., Coulter, K. J., Derbinsky, N., and Xu, J. 2012. The Soar User’s Manual Version 9.3.2. Computer Science and Engineering Department. University of Michigan. Unpublished manuscript.

  • Langley, P., and Choi, D. 2006. A unified cognitive architecture for physical agents. Paper presented at the Proceedings of the National Conference on Artificial Intelligence, 1469-1474, Boston MA, USA.

  • Langley, P., Laird, J. E., and Rogers, S. 2009. Cognitive architectures: Research issues and challenges. Cognitive Systems Research, 10(2):141-160.

  • Milner, D., and Goodale, M. A. 2008. Two visual systems re-viewed. Neuropsychologia, 46(3):774-785.

  • Rohrer, B. 2012. BECCA: Reintegrating AI for natural world interaction. Paper presented at the AAAI Spring Symposium on Designing Intelligent Robots: Reintegrating AI, Stanford California, USA.

  • Russell, S. J., and Norvig, P. 2009. Artificial intelligence: a modern approach (Third ed.): Prentice hall.

  • Searle, J. R. 1983. Intentionality: An essay in the philosophy of mind: Cambridge University Press.

  • Shapiro, S. C., and Bona, J. P. 2010. The GLAIR cognitive architecture. International Journal of Machine Consciousness, 2(2):307-332.

  • Sun, R. 2003. A tutorial on CLARION 5.0. Unpublished manuscript.

  • Sun, R. 2006. The CLARION cognitive architecture: Extending cognitive modeling to social simulation. In R. Sun (Ed.), Cognition and multi-agent interaction, 79-99. New York: Cambridge University Press.