Explanation-based learning: a problem solving perspective
Artificial Intelligence
Learning approximate control rules of high utility
Proceedings of the seventh international conference (1990) on Machine learning
The Utility of Knowledge in Inductive Learning
Machine Learning
Learning by analogical reasoning in general problem-solving
Learning by analogical reasoning in general problem-solving
Partial-order planning: evaluating possible efficiency gains
Artificial Intelligence
Incremental learning of control knowledge for nonlinear problem solving
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
On-Line Learning from Search Failures
Machine Learning
Failure driven dynamic search control for partial order planners: an explanation based approach
Artificial Intelligence
Learning Logical Definitions from Relations
Machine Learning
Explanation-Based Generalization: A Unifying View
Machine Learning
Explanation-Based Learning: An Alternative View
Machine Learning
Integrating Explanation-Based and Inductive Learning Techniques to AcquireSearch-Control for Planning
Toward a Model of Intelligence as an Economy of Agents
Machine Learning
Learning Control Knowledge for Forward Search Planning
The Journal of Machine Learning Research
Learning to improve both efficiency and quality of planning
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Journal of Artificial Intelligence Research
Inductive learning of search control rules for planning
Artificial Intelligence
Learning Linear Ranking Functions for Beam Search with Application to Planning
The Journal of Machine Learning Research
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Most research in planning and learning has involved linear, state-based planners. This paper presents SCOPE, a system for learning search-control rules that improve the performance of a partial-order planner. SCOPE integrates explanation-based and inductive learning techniques to acquire control rules for a partial-order planner. Learned rules are in the form of selection heuristics that help the planner choose between competing plan refinements. Specifically, SCOPE learns domain-specific control rules for a version of the UCPOP planning algorithm. The resulting system is shown to produce significant speedup in two different planning domains.