Domain-independent planning: representation and plan generation
Artificial Intelligence
Planning as search: a quantitative approach
Artificial Intelligence
Automatically generating abstractions for problem solving
Automatically generating abstractions for problem solving
Analyzing external conditions to improve the efficiency of HTN planning
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
The computational complexity of avoiding spurious states in state space abstraction
Artificial Intelligence
The expected value of hierarchical problem-solving
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
The complexity of action redundancy
AI*IA'05 Proceedings of the 9th conference on Advances in Artificial Intelligence
An approach to safe continuous planning
PRIMA'04 Proceedings of the 7th Pacific Rim international conference on Intelligent Agents and Multi-Agent Systems
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Using abstraction in planning does not guarantee an improvement in search efficiency; it is possible for an abstract planner to display worse performance than one that does not use abstraction. Analysis and experiments have shown that good abstraction hierarchies have, or are close to having, the downward refinement property, whereby, given that a concrete-level solution exists, every abstract solution can be refined to a concrete-level solution without backtracking across abstract levels. Working within a semantics for ABSTRIPS-style abstraction we provide a characterization of the downward refinement property. After discussing its effect on search efficiency, we develop a semantic condition sufficient for guaranteeing its presence in an abstraction hierarchy. Using the semantic condition, we then provide a set of sufficient and polynomial-time checkable syntactic conditions that can be used for checking a hierarchy for the downward refinement property.