Although researchers have studied planning since the early days of AI, recent developments have revolutionized the field. Furthermore, work on propositional planning is closely related to the algorithms used in the autonomous controller for the National Aeronautics and Space Administration (NASA) Deep Space One spacecraft, launched in October 1998. As a result, our understanding of interleaved planning and execution has advanced as well as the speed with which we can solve classical planning problems. The goal of this survey is to explain these recent advances and suggest new directions for research. Because this article requires minimal AI background (for example, simple logic and basic search algorithms), it's suitable for a wide audience, but my treatment is not exhaustive because I don't have the space to discuss every active topic of planning research.

In 1998, the international planning community was invited to take part in the first planning competition, hosted by the Artificial Intelligence Planning Systems Conference, to provide a new impetus for empirical evaluation and direct comparison of automatic domain-independent planning systems. This article describes the systems that competed in the event, examines the results, and considers some of the implications for the future of the field. Competitors were invited to come and compete on a collection of domains and associated problems, sight unseen, using whatever planning technology they wanted. (Anderson and Weld 1998). Another important problem was anticipating the demands of the competition domains.

The way work load is shared between these stages depends on the particular approach in use. The reader unfamiliar with Petri nets needs only know the following: They are made of places, transitions, and tokens. A place can be seen as a token holder. When it contains one or more tokens, it is said to be marked. Transitions allow tokens to circulate from place to place.

This planning graph structure is then fed to a heuristic extractor module that is capable of extracting a variety of effective and admissible heuristics, based on our recent theory (Nguyen and Kambhampati 2000). This heuristic, along with the problem specification, and the set of ground actions in the final action level of the planning graph structure are fed to a regression state search planner. To guide a regression search in the state space, a heuristic function needs to evaluate the cost of some set S of subgoals, comprising a regression state from the initial state, in terms of the number of actions required to achieve S from the initial state. This heuristic approximates the cost of a set S as the length of a "relaxed plan" for supporting S, ignoring all the mutex relations, plus the penalty for ignoring these negative interac-88 AI MAGAZINE Yochan is the planning group directed by Subbarao Kambhampati at Arizona State University.

Journal of Arti ial In telligen e Resear h 20 (2003) 139-144 Submitted 09/03; published 12/03 Commentary PDDL 2.1: Represen tation vs. Computation H e tor Gener he tor.geffner ICREA { Universitat Pomp eu F abr a Pase o de Cir unvala ion 8 08003 Bar elona, Sp ain Abstra t I ommen t on the PDDL 2.1 language and its use in the planning omp etition, fo using on the hoi es made for a ommo dating time and on urren y . I also dis uss some metho d-ologi al issues that ha v e to do with the mo v e to w ard more expressiv e planning languages and the balan e needed in planning resear h b et w een seman ti s and omputation. In tro du tion F o x and Long should b e thank ed and ongratulated for their eort in organizing and running the 3rd In ternational Planning Comp etition. They ame up with an extended planning language along with a n um b er of new problems and domains that hallenged existing planners and will ertainly hallenge future planners as w ell.

We describe and evaluate the algorithmic techniques that are used in the FF planning system. Like the HSP system, FF relies on forward state space search, using a heuristic that estimates goal distances by ignoring delete lists. Unlike HSP's heuristic, our method does not assume facts to be independent. We introduce a novel search strategy that combines hill-climbing with systematic search, and we show how other powerful heuristic information can be extracted and used to prune the search space. FF was the most successful automatic planner at the recent AIPS-2000 planning competition. We review the results of the competition, give data for other benchmark domains, and investigate the reasons for the runtime performance of FF compared to HSP.

This paper reviews the connections between Graphplan's planning-graph and the dynamic constraint satisfaction problem and motivates the need for adapting CSP search techniques to the Graphplan algorithm. It then describes how explanation based learning, dependency directed backtracking, dynamic variable ordering, forward checking, sticky values and random-restart search strategies can be adapted to Graphplan. Empirical results are provided to demonstrate that these augmentations improve Graphplan's performance significantly (up to 1000x speedups) on several benchmark problems. Special attention is paid to the explanation-based learning and dependency directed backtracking techniques as they are empirically found to be most useful in improving the performance of Graphplan.

The implementation is based on two insights: that many of the graph construction operations can be implemented as bit-level logical operations on bit vectors, and that the graph should not be explicitly constructed beyond the x point. A more detailed discussion of the competition, from the competitors' point of view, is in preparation. First, we observe that action pre-and post-conditions can be represented using bit vectors. Checking for mutual exclusion between pairs of actions which directly interact can be implemented using logical operations on these bit vectors. Mutual exclusion (mutex relations) between facts can be implemented in a similar way. Second, we observe that there is no advantage in explicit construction of the graph beyond the stage at which the x point is reached. Since no new facts, actions or mutex relations are added beyond the x point these goal sets can be considered without explicit copying of the fact and action layers. For example, using a heuristic discussed in Section 5.1, Sta In this paper we describe the spike and wave front mechanisms and provide experimental results indicating the performance advantages obtained. The layers correspond to snapshots of possible states at instants on a time line from the initial to the goal state.

The Graphplan algorithm for generating optimal make-span plans containing parallel sets of actions remains one of the most effective ways to generate such plans. However, despite enhancements on a range of fronts, the approach is currently dominated in terms of speed, by state space planners that employ distance-based heuristics to quickly generate serial plans. We report on a family of strategies that employ available memory to construct a search trace so as to learn from various aspects of Graphplan's iterative search episodes in order to expedite search in subsequent episodes. The planning approaches can be partitioned into two classes according to the type and extent of search experience captured in the trace. The planners using the more aggressive tracing method are able to avoid much of Graphplan's redundant search effort, while planners in the second class trade off this aspect in favor of a much higher degree of freedom than Graphplan in traversing the space of'states' generated during regression search on the planning graph. The tactic favored by the second approach, exploiting the search trace to transform the depth-first, IDA* nature of Graphplan's search into an iterative state space view, is shown to be the more powerful. We demonstrate that distance-based, state space heuristics can be adapted to informed traversal of the search trace used by the second class of planners and develop an augmentation targeted specifically at planning graph search. Guided by such a heuristic, the step-optimal version of the planner in this class clearly dominates even a highly enhanced version of Graphplan. By adopting beam search on the search trace we then show that virtually optimal parallel plans can be generated at speeds quite competitive with a modern heuristic state space planner.

In 1998, the international planning community was invited to take part in the first planning competition, hosted by the Artificial Intelligence Planning Systems Conference, to provide a new impetus for empirical evaluation and direct comparison of automatic domain-independent planning systems. This article describes the systems that competed in the event, examines the results, and considers some of the implications for the future of the field.