Planning for conjunctive goals
Artificial Intelligence
Principles of database and knowledge-base systems, Vol. I
Principles of database and knowledge-base systems, Vol. I
Explanation-based generalisation = partial evaluation
Artificial Intelligence
Explanation-based learning: a problem solving perspective
Artificial Intelligence
Learning by experimentation: the operator refinement method
Machine learning
Automatically generating abstractions for problem solving
Automatically generating abstractions for problem solving
A structural theory of explanation-based learning
A structural theory of explanation-based learning
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
Representation and automatic synthesis of reaction plans
Representation and automatic synthesis of reaction plans
Discovering admissible heuristics by abstracting and optimizing: a transformational approach
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 1
Multiple dimensions of generalization in model-based troubleshooting
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
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Explanation-Based Learning (EBL) can be used to significantly speed up problem solving. Is there sufficient structure in the definition of a problem space to enable a static analyzer, using EBL-style optimizations, to speed up problem solving without utilizing training examples? If so, will such an analyzer run in reasonable time? This paper demonstrates that for a wide range of problem spaces the answer to both questions is "yes." The STATIC program speeds up problem solving for the PRODIGY problem solver without utilizing training examples. In Minton's problem spaces [1988], STATIC acquires control knowledge from twenty six to seventy seven times faster, and speeds up PRODIGY up to three times as much as PRODIGY/EBL. This paper presents STATIC's algorithms, derives a condition under which STATIC is guaranteed to achieve polynomial-time problem solving, and contrasts STATIC with PRODIGY/EBL.