Several recent studies have compared the relative efficiency of alternative flaw selection strategies for partial-order causal link (POCL) planning. We review this literature, and present new experimental results that generalize the earlier work and explain some of the discrepancies in it. In particular, we describe the Least-Cost Flaw Repair (LCFR) strategy developed and analyzed by Joslin and Pollack (1994), and compare it with other strategies, including Gerevini and Schubert's (1996) ZLIFO strategy. LCFR and ZLIFO make very different, and apparently conflicting claims about the most effective way to reduce searchspace size in POCL planning. We resolve this conflict, arguing that much of the benefit that Gerevini and Schubert ascribe to the LIFO component of their ZLIFO strategy is better attributed to other causes. We show that for many problems, a strategy that combines least-cost flaw selection with the delay of separable threats will be effective in reducing search-space size, a...

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...

Many current and potential AI applications are intended to operate in dynamic environments, including those with multiple agents. As a result, standard AI plan-generation technology must be augmented with mechanisms for managing changing information, for focusing attention when multiple events occur, and for coordinating with other planning processes. The DIPART testbed (Distributed, Interactive Planner's Assistant for Real-time Transportation planning) was developed to serve as an experimental platform for analyzing a variety of such mechanisms. In this paper, we present an overview both of the DIPART system and of some of the methods for planning in dynamic environments that we have been investigating using DIPART. Many of these methods derive from theoretical work in real-time AI and in related fields, such as real-time operating systems. Introduction Many current and potential AI applications are intended to operate in dynamic environments, including those with multiple agents. An...

Recent trends in planning research have led to empirical comparison becoming commonplace. The field has started to settle...

Abstract. Discrete event sequences have been modeled with two types of representation: snapshots and overviews. Snapshot models describe the process as a collection of relatively short sequences. Overview models collect key relationships into a single structure, providing an integrated but abstract view. This paper describes a new algorithm for constructing one type of overview model: state transition diagrams. The algorithm, called State Transition Dependency Detection (STDD), is the latest in a family of statistics based algorithms for modeling event sequences called Dependency Detection. We present accuracy results for the algorithm on synthetic data and data from the execution of two AI systems. 1

The flaw selection strategy is integral to good performance in a partial order planner. Yet, no flaw selection strategy has been shown to be superior on all problems. Two prominent strategies, ZLIFO and LCFR, perform well on different problems. We studied three domain specific factors: precondition ordering in domain theories, goal ordering in problems and dynamic ordering of flaws. For each factor, we collected data on the performance of ZLIFO, LCFR and related strategies on systematically varied problems. We found that while all strategies are sensitive to these factors, some are more so than others. Moreover, even careful control of domain and problem definition in LIFO strategies cannot produce consistently better performance than Least Cost on all problems. Based on our data, the ultimate flaw selection strategy must be dynamic, rather than relying exclusively on any of the existing strategies. 1 Introduction As has been shown several times [Joslin and Pollack, 1994], [Srinivasan...