Practical planning: extending the classical AI planning paradigm
Practical planning: extending the classical AI planning paradigm
Characterizing multi-contributor causal structures for planning
Proceedings of the first international conference on Artificial intelligence planning systems
Conditional nonlinear planning
Proceedings of the first international conference on Artificial intelligence planning systems
A theory of conflict resolution in planning
Artificial Intelligence - Special volume on constraint-based reasoning
Partial-order planning: evaluating possible efficiency gains
Artificial Intelligence
A Computational Model of Skill Acquisition
A Computational Model of Skill Acquisition
Learning effective search control knowledge: an explanation-based approach
Learning effective search control knowledge: an explanation-based approach
Reviving partial order planning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
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McAllester and Rosenblitts' (1991) systematic nonlinear planner (SNLP) removes threats as they are discovered. In other planners such as SIPE (Wilkins, 1988), and NOAH (Sacerdoti, 1977), threat resolution is partially or completely delayed. In this paper, we demonstrate that planner efficiency may be vastly improved by the use of alternatives to these threat removal strategies. We discuss five threat removal strategies and prove that two of these strategies dominate the other three--resulting in a provably smaller search space. Furthermore, the systematicity of the planning algorithm is preserved for each of the threat removal strategies. Finally, we confirm our results experimentally using a large number of planning examples including examples from the literature.