Communications of the ACM
Macro-operators: a weak method for learning
Artificial Intelligence - Lecture notes in computer science 178
STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
Learnability and the Vapnik-Chervonenkis dimension
Journal of the ACM (JACM)
Quantitative results concerning the utility of explanation-based learning
Artificial Intelligence
COLT '89 Proceedings of the second annual workshop on Computational learning theory
Finding optimal derivation in redundant knowledge bases
Artificial Intelligence
Machine learning: a theoretical approach
Machine learning: a theoretical approach
Computational learning theory: an introduction
Computational learning theory: an introduction
Measuring utility and the design of provably good EBL algorithms
ML92 Proceedings of the ninth international workshop on Machine learning
On the complexity of blocks-world planning
Artificial Intelligence
A structural theory of explanation-based learning
Artificial Intelligence
An introduction to computational learning theory
An introduction to computational learning theory
An efficient context-free parsing algorithm
Communications of the ACM
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Learning Conjunctive Concepts in Structural Domains
Machine Learning
Machine Learning
Chunking in Soar: The Anatomy of a General Learning Mechanism
Machine Learning
Explanation-Based Generalization: A Unifying View
Machine Learning
Machine Learning
Explanation-Based Learning: An Alternative View
Machine Learning
Machine Learning
DYNAMIC: A New Role for Training Problems in EBL
ML '92 Proceedings of the Ninth International Workshop on Machine Learning
Learning Goal-Decomposition Rules using Exercises
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Approximating optimal policies for partially observable stochastic domains
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Learning investment functions for controlling the utility of control knowledge
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Machine Learning
Monotonic reductions, representative equivalence, and compilation of intractable problems
Journal of the ACM (JACM)
Speedup learning for repair-based search by identifying redundant steps
The Journal of Machine Learning Research
An integrated architecture for shallow and deep processing
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Learning strategies for story comprehension: a reinforcement learning approach
ICML '05 Proceedings of the 22nd international conference on Machine learning
Learning and applying competitive strategies
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
A selective macro-learning algorithm and its application to the N × N sliding-tile puzzle
Journal of Artificial Intelligence Research
Learning strategies for open-domain natural language question answering
ACLstudent '05 Proceedings of the ACL Student Research Workshop
Learning strategies for open-domain natural language question answering
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Fast Structured Prediction Using Large Margin Sigmoid Belief Networks
International Journal of Computer Vision
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Speedup learning seeks to improve the computational efficiency of problem solving with experience. In this paper, we develop a formal framework for learning efficient problem solving from random problems and their solutions. We apply this framework to two different representations of learned knowledge, namely control rules and macro-operators, and prove theorems that identify sufficient conditions for learning in each representation. Our proofs are constructive in that they are accompanied with learning algorithms. Our framework captures both empirical and explanation-based speedup learning in a unified fashion. We illustrate our framework with implementations in two domains: symbolic integration and Eight Puzzle. This work integrates many strands of experimental and theoretical work in machine learning, including empirical learning of control rules, macro-operator learning, Explanation-Based Learning (EBL), and Probably Approximately Correct (PAC) Learning.