Domain-independent planning: representation and plan generation
Artificial Intelligence
Planning as search: a quantitative approach
Artificial Intelligence
Explanation-based learning: a problem solving perspective
Artificial Intelligence
Learning hierarchies of abstraction spaces
Proceedings of the sixth international workshop on Machine learning
Mechanical Discovery of Classes of Problem-Solving Strategies
Journal of the ACM (JACM)
The Design and Analysis of Computer Algorithms
The Design and Analysis of Computer Algorithms
Learning effective search control knowledge: an explanation-based approach
Learning effective search control knowledge: an explanation-based approach
Human Problem Solving
STRIPS: a new approach to the application of theorem proving to problem solving
IJCAI'71 Proceedings of the 2nd international joint conference on Artificial intelligence
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 2
Abstraction in problem solving and learning
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Discovering admissible heuristics by abstracting and optimizing: a transformational approach
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
A computational model for multiple goals
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Synthesizing plans for multiple domains
SARA'05 Proceedings of the 6th international conference on Abstraction, Reformulation and Approximation
What Defaults Can Do That Hierarchies Can't
Fundamenta Informaticae
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The use of abstraction in problem solving is an effective approach to reducing search, but finding good abstractions is a difficult problem, even for people. This paper identifies a criterion for selecting useful abstractions, describes a tractable algorithm for generating them, and empirically demonstrates that the abstractions reduce search. The abstraction learner, called ALPINE, is integrated with the PRODIGY problem solver [Minton et al., 1989b, Carbonell et al., 1990] and has been tested on large problem sets in multiple domains.