• Publications
  • Influence
The Fast Downward Planning System
  • M. Helmert
  • Computer Science, Mathematics
  • J. Artif. Intell. Res.
  • 1 May 2006
Fast Downward is a classical planning system based on heuristic search. It can deal with general deterministic planning problems encoded in the propositional fragment of PDDL2.2, including advancedExpand
  • 1,197
  • 240
Landmarks, Critical Paths and Abstractions: What's the Difference Anyway?
Current heuristic estimators for classical domain-independent planning are usually based on one of four ideas: delete relaxations, critical paths, abstractions, and, most recently, landmarks.Expand
  • 366
  • 41
Landmarks Revisited
Landmarks for propositional planning tasks are variable assignments that must occur at some point in every solution plan. We propose a novel approach for using landmarks in planning by deriving aExpand
  • 232
  • 41
Concise finite-domain representations for PDDL planning tasks
  • M. Helmert
  • Computer Science
  • Artif. Intell.
  • 1 April 2009
We introduce an efficient method for translating planning tasks specified in the standard PDDL formalism into a concise grounded representation that uses finite-domain state variables instead of theExpand
  • 217
  • 38
Flexible Abstraction Heuristics for Optimal Sequential Planning
We describe an approach to deriving consistent heuristics for automated planning, based on explicit search in abstract state spaces. The key to managing complexity is interleaving composition ofExpand
  • 246
  • 26
A Planning Heuristic Based on Causal Graph Analysis
In recent years, heuristic search methods for classical planning have achieved remarkable results. Their most successful representative, the FF algorithm, performs well over a wide spectrum ofExpand
  • 229
  • 24
Domain-Independent Construction of Pattern Database Heuristics for Cost-Optimal Planning
Heuristic search is a leading approach to domain-independent planning. For cost-optimal planning, however, existing admissible heuristics are generally too weak to effectively guide the search.Expand
  • 194
  • 19
Merge-and-Shrink Abstraction: A Method for Generating Lower Bounds in Factored State Spaces
Many areas of computer science require answering questions about reachability in compactly described discrete transition systems. Answering such questions effectively requires techniques to be ableExpand
  • 127
  • 15
A Stochastic Local Search Approach to Vertex Cover
We introduce a novel stochastic local search algorithm for the vertex cover problem. Compared to current exhaustive search techniques, our algorithm achieves excellent performance on a suite ofExpand
  • 68
  • 15
Complexity results for standard benchmark domains in planning
  • M. Helmert
  • Mathematics, Computer Science
  • Artif. Intell.
  • 1 February 2003
The efficiency of AI planning systems is usually evaluated empirically. For the validity of conclusions drawn from such empirical data, the problem set used for evaluation is of critical importance.Expand
  • 123
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