Chunking in Soar: The Anatomy of a General Learning Mechanism
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.
Cite
Text
Laird et al. "Chunking in Soar: The Anatomy of a General Learning Mechanism." Machine Learning, 1986. doi:10.1023/A:1022639103969Markdown
[Laird et al. "Chunking in Soar: The Anatomy of a General Learning Mechanism." Machine Learning, 1986.](https://mlanthology.org/mlj/1986/laird1986mlj-chunking/) doi:10.1023/A:1022639103969BibTeX
@article{laird1986mlj-chunking,
title = {{Chunking in Soar: The Anatomy of a General Learning Mechanism}},
author = {Laird, John E. and Rosenbloom, Paul S. and Newell, Allen},
journal = {Machine Learning},
year = {1986},
pages = {11-46},
doi = {10.1023/A:1022639103969},
volume = {1},
url = {https://mlanthology.org/mlj/1986/laird1986mlj-chunking/}
}