Theory of linear and integer programming
Theory of linear and integer programming
The computational complexity of propositional STRIPS planning
Artificial Intelligence
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Machine Discovery of Effective Admissible Heuristics
Machine Learning
Constraint Processing
An interior-point approach for primal block-angular problems
Computational Optimization and Applications
New admissible heuristics for domain-independent planning
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Domain-independent construction of pattern database heuristics for cost-optimal planning
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Accuracy of admissible heuristic functions in selected planning domains
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Additive pattern database heuristics
Journal of Artificial Intelligence Research
The fast downward planning system
Journal of Artificial Intelligence Research
New Islands of tractability of cost-optimal planning
Journal of Artificial Intelligence Research
A general theory of additive state space abstractions
Journal of Artificial Intelligence Research
Cost-optimal planning with landmarks
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Friends or foes? on planning as satisfiability and abstract CNF encodings
Journal of Artificial Intelligence Research
An LP-based heuristic for optimal planning
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Implicit abstraction heuristics
Journal of Artificial Intelligence Research
Landmark-enhanced abstraction heuristics
Artificial Intelligence
Implicit abstraction heuristics for cost-optimal planning
AI Communications
Getting the most out of pattern databases for classical planning
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Additive ensembles of admissible heuristics constitute the most general form of exploiting the individual strengths of numerous admissible heuristics in optimal planning. However, the same set of heuristics can be additively composed in infinitely many ways and the quality of the resulting heuristic estimate depends directly on the choice of the composition. Focusing on abstraction heuristics, we describe a procedure that takes a deterministic planning problem, a forward-search state, and a set of abstraction-based admissible heuristics, and derives an optimal additive composition of these heuristics with respect to the given state. Most importantly, we show that this procedure is polynomial-time for arbitrary sets of all abstraction heuristics with which we are acquainted, including explicit abstractions such as pattern databases (regular or constrained) and merge-and-shrink, and implicit abstractions such as fork-decomposition and abstractions based on tractable constraint optimization over tree-shaped constraint networks.