Encyclopedia of the Sciences of Learning

2012 Edition
| Editors: Norbert M. Seel

Long-Term Learning in Soar

  • William G. KennedyEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-1-4419-1428-6_1875



Long-term learning in Soar is the process of accumulating procedural knowledge throughout the existence of an intelligent, learning agent. In Soar, such knowledge is in the form of IF-THEN productions, or rules, called “chunks.” The learned chunks are new rules capturing the results of resolving obstacles in the reasoning process. This long-term knowledge is maintained by the system with the expectation that it will be useful during the existence of the agent. Declarative memory is not part of Soar’s long-term memory.

Theoretical Background

Allen Newell, through his book, “Unified Theories of Cognition” (Newell 1990), proposed many partial theories of cognition, both abstract and human, and offered the Soar architecture as a candidate-unified theory of cognition. He defined a unified theory of cognition as “a single set of mechanisms for all of cognitive behavior.” Three parts of his theory are...

This is a preview of subscription content, log in to check access.


  1. Doorenbos, R. (1993). Matching 100,000 learned rules. Proceedings of the eleventh national conference on artificial intelligence (pp. 290–296). Menlo Park, CA: AAAI Press.Google Scholar
  2. Kennedy, W. G., & De Jong, K. A. (2003) Characteristics of long-term learning in soar and its application to the utility problem. Proceedings of the twentieth international conference on machine learning (pp. 337–344). Menlo Park, CA: AAAI Press.Google Scholar
  3. Kennedy, W. G., & Trafton, J. G. (2007). Long-term symbolic learning. Cognitive Systems Research, 8, 237–247.Google Scholar
  4. Laird, J. E. (2008) Extending the soar cognitive architecture. In Artificial general intelligence 2008: Proceedings of the first AIG conference. Memphis, TN: ISO Press.Google Scholar
  5. Minton, S. (1990). Quantitative results concerning the utility of explanation-based learning. Artificial Intelligence, 42, 363–391.Google Scholar
  6. Newell, A. (1990). Unified theories of cognition. Cambridge, MA: Harvard University Press.Google Scholar
  7. Tambe, M., Newell, A., & Rosenbloom, P. S. (1990). The problem of expensive chunks and its solution by restricting expressiveness. Machine Learning, 5, 299–348.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Krasnow Institute for Advanced StudyGeorge Mason UniversityFairfaxUSA