In recent years research in the planning community has moved increasingly towards application of planners to realistic problems involving both time and many types of resources. For example, interest in planning demonstrated by the space research community has inspired work in observation scheduling, planetary rover exploration and spacecraft control domains. Other temporal and resource-intensive domains including logistics planning, plant control and manufacturing have also helped to focus the community on the modelling and reasoning issues that must be confronted to make planning technology meet the challenges of application. The International Planning Competitions have acted as an important motivating force behind the progress that has been made in planning since 1998. The third competition (held in 2002) set the planning community the challenge of handling time and numeric resources. This necessitated the development of a modelling language capable of expressing temporal and numeric properties of planning domains. In this paper we describe the language, pddl2.1, that was used in the competition. We describe the syntax of the language, its formal semantics and the validation of concurrent plans. We observe that pddl2.1 has considerable modelling power — exceeding the capabilities of current planning technology — and presents a number of important challenges to the research community.

In the recent AIPS98 Planning Competition, the hsp planner, based on a forward state search and a domain-independent heuristic, showed that heuristic search planners can be competitive with state of the art Graphplan and Satisfiability planners. hsp solved more problems than the other planners but it often took more time or produced longer plans. The main bottleneck in hsp is the computation of the heuristic for every new state. This computation may take up to 85% of the processing time. In this paper, we present a solution to this problem that uses a simple change in the direction of the search. The new planner, that we call hspr, is based on the same ideas and heuristic as hsp, but searches backward from the goal rather than forward from the initial state. This allows hspr to compute the heuristic estimates only once. As a result, hspr can produce better plans, often in less time. For example, hspr solves each of the 30 logistics problems from Kautz and Selman in less than 3 seconds. This is two orders of magnitude faster than blackbox. At the same time

The SHOP2 planning system received one of the awards for distinguished performance in the 2002 International Planning Competition. This paper describes the features of SHOP2 which enabled it to excel in the competition, especially those aspects of SHOP2 that deal with temporal and metric planning domains.

This paper reports the outcome of the third in the series of biennial international planning competitions, held in association with the International Conference on AI Planning and Scheduling (AIPS) in 2002. In addition to describing the domains, the planners and the objectives of the competition, the paper includes analysis of the results. The results are analysed from several perspectives, in order to address the questions of comparative performance between planners, comparative difficulty of domains, the degree of agreement between planners about the relative difficulty of individual problem instances and the question of how well planners scale relative to one another over increasingly difficult problems. The paper addresses these questions through statistical analysis of the raw results of the competition, in order to determine which results can be considered to be adequately supported by the data. The paper concludes with a discussion of some challenges for the future of the competition series. 1.

We explore approximate policy iteration (API), replacing the usual costfunction learning step with a learning step in policy space. We give policy-language biases that enable solution of very large relational Markov decision processes (MDPs) that no previous technique can solve.

There has been significant recent progress in reasoning and constraint processing methods. In areas such as planning and finite model-checking, current solution techniques can handle combinatorial problems with up to a million variables and five million constraints. The good scaling behavior of these methods appears to defy what one would expect based on a worst-case complexity analysis. In order to bridge this gap between theory and practice, we propose a new framework for studying the complexity of these techniques on practical problem instances. In particular, our approach incorporates general structural properties observed in practical problem instances into the formal complexity

We present TALplanner, a forward-chaining planner based on the use of domaindependent search control knowledge represented as formulas in the Temporal Action Logic (TAL). TAL is a narrative based linear metric time logic used for reasoning about action and change in incompletely specied dynamic environments. TAL is used as the formal semantic basis for TALplanner, where a TAL goal narrative with control formulas is input to TALplanner which then generates a TAL narrative that entails the goal and control formulas. The sequential version of TALplanner is presented. The expressivity of plan operators is then extended to deal with an interesting class of resource types. An algorithm for generating concurrent plans, where operators have varying durations and internal state, is also presented. All versions of TALplanner have been implemented. The potential of these techniques is demonstrated by applying TALplanner to a number of standard planning benchmarks in the literature.

Recently tremendous advances have been made in the performance of AI planning systems. However increased performance is only one of the prerequisites for bringing planning into the realm of real applications; advances in the scope of problems that can be represented and solved must also be made. In this paper we address two important representational features, concurrently executable actions with varying durations, and metric quantities like resources, both essential for modeling real applications. We show how the forward chaining approach to planning can be extended to allow it to solve planning problems with these two features. Forward chaining using heuristics or domain specific information to guide search has shown itself to be a very promising approach to planning, and it is sensible to try to build on this success. In our experiments we utilize the TLPLAN approach to planning, in which declaratively represented control knowledge is used to guide search. We show that this extra knowledge can be intuitive and easy to obtain, and that with it impressive planning performance can be achieved. 1

One of the more controversial recent planning algorithms is the SHOP algorithm, an HTN planning algorithm that plans for tasks in the same order that they are to be executed. SHOP can use domaindependent knowledge to generate plans very quickly, but it can be difficult to write good knowledge bases for SHOP. Our hypothesis is that this difficulty is because SHOP’s total-ordering requirement for the subtasks of its methods is more restrictive than it needs to be. To examine this hypothesis, we have developed a new HTN planning algorithm called SHOP2. Like SHOP, SHOP2 is sound and complete, and it constructs plans in the same order that they will later be executed. But unlike SHOP, SHOP2 allows the subtasks of each

Planning for extended goals in non-deterministic domains is one of the most significant and challenging planning problems. In spite