We propose some domain-independent techniques for bringing well-founded partialorder 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 selecti...

We describe some simple domain-independent improvements to plan-refinement strategies for well-founded partial order planning that promise to bring this style of planning closer to practicality. One suggestion concerns the strategy for selecting plans for refinement among the current (incomplete) candidate plans. We propose an A* heuristic that counts only steps and open conditions, while ignoring "unsafe conditions" (threats). A second suggestion concerns the strategy for selecting open conditions (goals) to be established next in a selected incomplete plan. Here we propose a variant of a strategy suggested by Peot & Smith and studied by Joslin & Pollack; the variant gives top priority to unmatchable open conditions (enabling the elimination of the plan), secondhighest priority to goals that can only be achieved uniquely, and otherwise uses LIFO prioritization. The preference for uniquely achievable goals is a "zerocommitment " strategy in the sense that the corresponding plan refinem...

To date, no one planner has demonstrated clearly superior performance. Although researchers have hypothesized that this should be the case, no one has performed a large study to test its limits. In this research, we tested performance of a set of planners to determine which is best on what types of problems. The study included six planners and over 200 problems. We found that performance, as measured by number of problems solved and computation time, varied with no one planner solving all the problems or being consistently fastest. Analysis of the data also showed that most planners either fail or succeed quickly and that performance depends at least in part on some easily observable problem/domain features. Based on these results, we implemented a meta-planner that interleaves execution of six planners on a problem until one of them solves it. The control strategy for ordering the planners and allocating time is derived from the performance study data. We found that our meta-planner is able to solve more problems than any single planner, but at the expense of computation time.

We show that by inferring parameter domains of planning operators, given the definitions of the operators and the initial and goal conditions, we can often speed up the planning process by an order of magnitude or more. We infer parameter domains by a polynomial-time algorithm that uses forward propagation of sets of constants occurring in the initial conditions and in operator postconditions. During planning, parameter domains can be used to prune operator instances whose parameter domains are inconsistent with binding constraints, and to eliminate spurious "clobbering threats" that cannot, in fact, be realized without violating domain constraints. We illustrate these applications with examples from the UCPOP test suite and from the Rochester trains transportation planning domain. Correspondence: please address correspondence to the second author. This work is a revised and extended version of a paper that will appear in Proceedings of the Third International Conference on Artifici...

There is controversy as to whether explicit support for PDDL-like axioms and derived predicates is needed for planners to handle real-world domains effectively. Many researchers have deplored the lack of precise semantics for such axioms, while others have argued that it might be best to compile them away. We propose an adequate semantics for PDDL axioms and show that they are an essential feature by proving that it is impossible to compile them away if we restrict the growth of plans and domain descriptions to be polynomial. These results suggest that adding a reasonable implementation to handle axioms inside the planner is beneficial for the performance. Our experiments confirm this suggestion.

When a user is engaged in selecting one of many options, a useful software assistant can help by organizing the available information as well as advising the user when they become distracted. For this advice to be effective, it must be convincing to the user. This paper explores the use of planning to construct such arguments using the operators of Maybury (1993). The software assistant can detect any of four selection errors by comparing the previous and current models of the world, and it can use planning to construct an argument of English text that describes the inappropriate action the user took and what they should do next to expedite the task.