Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
A general explanation-based learning mechanism and its application to narrative understanding
A general explanation-based learning mechanism and its application to narrative understanding
Generalizing the structure of explanations in explanation-based learning
Generalizing the structure of explanations in explanation-based learning
Empirical explorations with the logic theory machine: a case study in heuristics
Computers & thought
Explanation-Based Generalization: A Unifying View
Machine Learning
Explanation-Based Learning: An Alternative View
Machine Learning
Learning effective search control knowledge: an explanation-based approach
Learning effective search control knowledge: an explanation-based approach
POIROT: acquiring workflows by combining models learned from interpreted traces
Proceedings of the fifth international conference on Knowledge capture
Inductive learning of search control rules for planning
Artificial Intelligence
The utility of ebl in recursive domain theories
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
Empirical analysis of the general utility problem in machine learning
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Learning teleoreactive logic programs from problem solving
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Cognitive Systems Research
Acquisition of hierarchical reactive skills in a unified cognitive architecture
Cognitive Systems Research
Hi-index | 0.00 |
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 arc used. These effects help explain why previous experiments have obtained a variety of different results concerning the impact of explanation-based learning on performance.