The Effect of Rule Use on the Utility of Explanation-Based Learning (1989)
Raymond J. Mooney
The utility problem in explanation-based learning concerns the ability of learned rules or plans to actually improve the performance of a problem solving system. Previous research on this problem has focused on the amount, content, or form of learned information. This paper examines the effect of the use of learned information on performance. Experiments and informal analysis show that unconstrained use of learned rules eventually leads to degraded performance. However, constraining the use of learned rules helps avoid the negative effect of learning and lead to overall performance improvement. Search strategy is also shown to have a substantial effect on the contribution of learning to performance by affecting the manner in which learned rules are used. These effects help explain why previous experiments have obtained a variety of different results concerning the impact of explanation-based learning on performance.
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In Proceedings of the 11th International Joint Conference on Artificial Intelligence, pp. 725-730 1989. San Francisco, CA: Morgan Kaufmann.
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People
Raymond J. Mooney Faculty mooney [at] cs utexas edu
Areas of Interest
Explanation-Based Learning Learning for Planning and Problem Solving Machine Learning
Labs
Machine Learning