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
Efficient reinforcement learning
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
The computational complexity of propositional STRIPS planning
Artificial Intelligence
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Learning to reason with a restricted view
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
The Architecture of Cognition
Learning Conjunctive Concepts in Structural Domains
Machine Learning
Machine Learning
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Chunking in Soar: The Anatomy of a General Learning Mechanism
Machine Learning
Explanation-Based Generalization: A Unifying View
Machine Learning
Explanation-Based Learning: An Alternative View
Machine Learning
The complexity of theorem-proving procedures
STOC '71 Proceedings of the third annual ACM symposium on Theory of computing
A formal framework for speedup learning from problems and solutions
Journal of Artificial Intelligence Research
Learning disjunction of conjunctions
IJCAI'85 Proceedings of the 9th 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
Journal of the ACM (JACM)
Learning first order universal Horn expressions
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
STOC '99 Proceedings of the thirty-first annual ACM symposium on Theory of computing
Learning to Reason with a Restricted View
Machine Learning
Machine Learning
A neuroidal architecture for cognitive computation
Journal of the ACM (JACM)
Learning Range Restricted Horn Expressions
EuroCOLT '99 Proceedings of the 4th European Conference on Computational Learning Theory
Learning measures of progress for planning domains
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
A formal framework for speedup learning from problems and solutions
Journal of Artificial Intelligence Research
Learning action strategies for planning domains using genetic programming
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
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We formalize a model for supervised learning of action strategies in dynamic stochastic domains, and show that pac-learning results on Occam algorithms hold in this model as well. We then identify a particularly useful bias for action strategies based on production rule systems. We show that a subset of production rule systems, including rules in predicate calculus style, smaIl hidden state, and unobserved support predicates, is properly learnable. The bias we introduce enables the learning algorithm to invent the recursive support predicates which are used in the action strategy, and to reconstruct the internal state of the strategy. It is also shown that hierarchical strategies are learnable if a helpful teacher is available, but that otherwise the problem is computationally hard.