Communications of the ACM
Macro-operators: a weak method for learning
Artificial Intelligence - Lecture notes in computer science 178
Information Processing Letters
Quantitative results concerning the utility of explanation-based learning
Artificial Intelligence
COLT '89 Proceedings of the second annual workshop on Computational learning theory
Machine learning: a theoretical approach
Machine learning: a theoretical approach
A Study of Explanation-Based Methods for Inductive Learning
Machine Learning
Chunking in Soar: The Anatomy of a General Learning Mechanism
Machine Learning
Explanation-Based Generalization: A Unifying View
Machine Learning
Incorporating redundant learned rules: a preliminary formal analysis of EBL
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Measuring and improving the effectiveness of representations
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
A formalization of explanation-based macro-operator learning
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
| Hi-index | 0.00 |
Speedup learning seeks to improve the efficiency of search-based problem solvers. In this paper, we propose a new theoretical model of speedup learning which captures systems that improve problem solving performance by solving a user-given set of problems. We also use this model to motivate the notion of "batch problem solving," and argue that it is more congenial to learning than sequential problem solving. Our theoretical results are applicable to all serially decomposable domains. We empirically validate our results in the domain of Eight Puzzle.