Communications of the ACM
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
Computational learning theory: an introduction
Computational learning theory: an introduction
An introduction to computational learning theory
An introduction to computational learning theory
Probably approximately correct learning
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
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A framework was presented by Natarajan (B.K. Natarajan, On learning from exercises, in: Computational Learning Theory: Proceedings of the Second Annual Workshop, Morgan Kaufmann, Santa Cruz, CA, 1989, pp. 72-87) for the speedup learning of search algorithms. Speedup learning solves the problem of using teacher-provided examples to guide the improvement of algorithm efficiency. In this paper we extend their results by identifying two classes, the emonomial and emonotonic domains. We show that, in the Natarajan model, the speedup learning problem is solvable for the latter but not for the former. We outline further promising lines of investigation that may lead to a more comprehensive classification of the difficulty of speedup learning over a range of domains.