Accelerating Partial-Order Planners: Some Techniques for Effective Search Control and Pruning

Abstract

We propose some domain-independent techniques for bringing well-founded partial-order planners closer to practicality. The first two techniques are aimed at improving search control while keeping overhead costs low. One is based on a simple adjustment to the default A* heuristic used by UCPOP to select plans for refinement. The other is based on preferring ``zero commitment'' (forced) plan refinements whenever possible, and using LIFO prioritization otherwise. A more radical technique is the use of operator parameter domains to prune search. These domains are initially computed from the definitions of the operators and the initial and goal conditions, using a polynomial-time algorithm that propagates sets of constants through the operator graph, starting in the initial conditions. During planning, parameter domains can be used to prune nonviable operator instances and to remove spurious clobbering threats. In experiments based on modifications of UCPOP, our improved plan and goal selection strategies gave speedups by factors ranging from 5 to more than 1000 for a variety of problems that are nontrivial for the unmodified version. Crucially, the hardest problems gave the greatest improvements. The pruning technique based on parameter domains often gave speedups by an order of magnitude or more for difficult problems, both with the default UCPOP search strategy and with our improved strategy. The Lisp code for our techniques and for the test problems is provided in on-line appendices.

Cite this paper

@article{gerevini1996accelerating,
  title = {{Accelerating Partial-Order Planners: Some Techniques for Effective Search Control and Pruning}},
  author = {Gerevini, Alfonso and Schubert, Lenhart K.},
  journal = {Journal of Artificial Intelligence Research},
  volume = {5},
  pages = {95--137},
  year = {1996},
  doi = {10.1613/jair.316}
}

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