Machine Learning

, Volume 1, Issue 1, pp 11–46

Chunking in Soar: The anatomy of a general learning mechanism

  • John E. Laird
  • Paul S. Rosenbloom
  • Allen Newell
Article

DOI: 10.1007/BF00116249

Cite this article as:
Laird, J.E., Rosenbloom, P.S. & Newell, A. Mach Learn (1986) 1: 11. doi:10.1007/BF00116249

Abstract

In this article we describe an approach to the construction of a general learning mechanism based on chunking in Soar. Chunking is a learning mechanism that acquires rules from goal-based experience. Soar is a general problem-solving architecture with a rule-based memory. In previous work we have demonstrated how the combination of chunking and Soar could acquire search-control knowledge (strategy acquisition) and operator implementation rules in both search-based puzzle tasks and knowledge-based expert-systems tasks. In this work we examine the anatomy of chunking in Soar and provide a new demonstration of its learning capabilities involving the acquisition and use of macro-operators.

Key words

learning from experiencegeneral learning mechanismsproblem solvingchunkingproduction systemsmacro-operatorstransfer
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Copyright information

© Kluwer Academic Publishers 1986

Authors and Affiliations

  • John E. Laird
    • 1
  • Paul S. Rosenbloom
    • 2
    • 3
  • Allen Newell
    • 4
  1. 1.Intelligent Systems LaboratoryXerox Palo Alto Research CenterPalo AltoU.S.A.
  2. 2.Department of Computer ScienceStanford UniversityStanfordU.S.A.
  3. 3.Department of PsychologyStanford UniversityStanfordU.S.A.
  4. 4.Department of Computer ScienceCarnegie-Mellon UniversityPittsburghU.S.A.