Admissible Heuristics for Optimal Planning

By Patrik Haslum, Hector Geffner. International Conference on Artificial Intelligence Planning Systems, 2000

428 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/8e1a9698f70cca18787196f5e43e7811ef6e0cb2

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In Artificial Intelligence

Citations in the corpus

Anytime Heuristic Search (2011, methodology): “…Bonet’s publicly-available implementation of HSPr, we compared the performance of A* and Anytime WA* on benchmark problems from previous planning competitions that could be solved optimally by A*, using the domain-independent and admissible max-pair heuristic described by Haslum and Geffner (2000).”
Automated Planning and Acting (2016, background): “They approximate the cost of achieving a goal g by the cost of achieving the most costly subset of size m [261, 259].”
Constraint-Based Attribute and Interval Planning (2003, methodology): “For example, techniques like consistency enforcement [12, 13], no-good reasoning during planning [4], satisfiability techniques [14], domain independent heuristics [8, 9] and the use of constraint reasoning systems employing linear programming [21] have been used in different planners.”
"Distance"? Who Cares? Tailoring Merge-and-Shrink Heuristics to Detect Unsolvability (2014, methodology): “We did not run h heuristics (for m > 2) (Haslum and Geffner 2000) and pattern database heuristics (Haslum et al.”
Plan Recognition as Planning (2009, methodology): “Algorithms for computing optimal plans cast the planning problem as a problem of heuristic search with admissible heuristics derived automatically from the problem encoding [Haslum and Geffner, 2000] .”
Towards Clause-Learning State Space Search: Learning to Recognize Dead-Ends (2016, background): “In the original critical-path heuristics h m (Haslum and Geffner 2000), the atomic conjunctions are all conjunctions of size ≤ m , where m is a parameter.”
Fast Detection of Unsolvable Planning Instances Using Local Consistency (2013, methodology): “2007) and h (Haslum and Geffner 2000), that have certain similarities with our method, and that can also be used for consistency checking.”
Landmarks, Critical Paths and Abstractions: What's the Difference Anyway? (2009, background): “The h m heuristics (Haslum and Geffner 2000) estimate goal distances by computing lowerbound estimates on the cost of achieving sets of facts of cardinality m, where m ∈ N 1 is a parameter.”