SOAR: an architecture for general intelligence
Artificial Intelligence
Soar/PSM-E: investigating match parallelism in a learning production sytsem
PPEALS '88 Proceedings of the ACM/SIGPLAN conference on Parallel programming: experience with applications, languages and systems
A preliminary analysis of the Soar architecture as a basis for general intelligence
Artificial Intelligence
Eliminating combinatorics from production match
Eliminating combinatorics from production match
Match algorithms for generalized Rete networks
Artificial Intelligence
Information Filtering: Selection Mechanisms in Learning Systems
Machine Learning
Chunking in Soar: The Anatomy of a General Learning Mechanism
Machine Learning
Explanation-Based Generalization: A Unifying View
Machine Learning
Explanation-Based Learning: An Alternative View
Machine Learning
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Bounding the cost of learned rules: a transformational approach
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
A transformational analysis of the EBL utility problem
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
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Many learning systems suffer from the utility problem; that is, that time after learning is greater than time before learning. Discovering how to assure that learned knowledge will in fact speed up system performance has been a focus of research in explanation-based learning (EBL). One way to analyze the utility problem is by examining the differences between the match process (match search) of the learned rule and the problem-solving process from which it is learned. Prior work along these lines examined one such difference. It showed that if the search-control knowledge used during problem solving is not maintained in the match process for learned rules, then learning can engender a slowdown; but that this slowdown could be eliminated if the match is constrained by the original search-control knowledge. This article examines a second difference - when the structure of the problem solving differs from the structure of the match process for the learned rules, time after learning can be greater than time before learning. This article also shows that this slowdown can be eliminated by making the learning mechanism sensitive to the problem-solving structure; i.e., by reflecting such structure in the match of the learned rule.