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.

Many planning domains require a richer notion of time in which actions can overlap and have different durations. The key to fast performance in classical planners (e.g., Graphplan, ipp, and Blackbox) has been the use of a disjunctive representation with powerful mutual exclusion reasoning. This paper presents TGP a new algorithm for temporal planning. TGP operates

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.

An autonomous robot o ers a challenging and ideal field for the study of intelligent architectures. Autonomy within a rational behavior could be evaluated by the robot's effectiveness and robustness in carrying out tasks in different and ill-known environments. It raises major requirements on the control architecture. Furthermore, a robot as a programmable machine brings up other architectural needs, such asthe ease and quality of its specification and programming. This paper describes an integrated architecture allowing a mobile robot to plan its tasks, taking into account temporal and domain constraints, to perform corresponding actions and to control their execution in real-time, while being reactive to possible events. The general architecture is composed of three levels: a decision level, an execution level and a functional level. The later is composed of modules that embed the functions achieving sensor data processing and e ector control. The decision level is goal and event-driven, it may have several layers, according to the application; their basic structure is a planner/supervisor pair that enables to integrate deliberation and reaction. The proposed architecture relies naturally on several representations, programming paradigms and processing approaches meeting the precise requirements specified for each level. We developed proper tools to meet these specifications and implement each level of the architecture: IxTeT a temporal planner, prs a procedural system for task refinement and supervision, Kheops for the reactive control of the functional level, and G en oM for the specification and integration of modules at that level. Validation of temporal and logical properties of the reactive parts of the system, through these tools, are presented. Instances of the proposed architecture have been already integrated into several indoor and outdoor robots. Examples from real world experimentations are provided and analyzed.

On May 17th 1999, NASA activated for the first time an AI-based planner/scheduler running on the flight processor of a spacecraft. This was part of the Remote Agent Experiment (RAX), a demonstration of closedloop planning and execution, and model-based state inference and failure recovery. This paper describes the RAX Planner/Scheduler (RAX-PS), both in terms of the underlying planning framework and in terms of the fielded planner. RAX-PS plans are networks of constraints, built incrementally by consulting a model of the dynamics of the spacecraft. The RAX-PS planning procedure is formally well defined and can be proved to be complete. RAX-PS generates plans that are temporally flexible, allowing the execution system to adjust to actual plan execution conditions without breaking the plan. The practical aspect, developing a mission critical application, required paying attention to important engineering issues such as the design of methods for programmable search contr...

. This paper outlines the basic principles underlying reasoning about resources in IPP, which is a classical planner based on planning graphs originally introduced with the graphplan system. The main idea is to deal with resources in a strictly action-centered way, i.e., one specifies how each action consumes or produces resources, but no explicit temporal model is used. This avoids the computational problems of solving general constraint satisfaction problems by using instead interval arithmetics and propagation of resource requirements over time steps in the planning graph. 1 Actions that provide, produce, and consume Resources The starting point for the language extension is the ADL subset that is available in IPP 3.0 [7]. It offers universally quantified and conditional effects, atomic negation, equality as well as quantified and conditional goals. To reason about resources, an action description is extended in the following way: 1. Following the "ordinary preconditions" (which a...

We present an algorithm for planning with time and resources based on heuristic search. The algorithm minimizes makespan using an admissible heuristic derived automatically from the problem instance. Estimators for resource consumption are derived in the same way. The goals are twofold: To show the flexibility of the heuristic search approach to planning and to develop a planner that combines expressivity and performance. The two main issues are the definition of regression in the temporal setting and the definition of the heuristic for estimating completion time. A number of experiments are presented for assessing the performance of the resulting planner. 1

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

This article studies the problem of modifying the action ordering of a plan in order to optimise the plan according to various criteria. One of these criteria is to make a plan less constrained and the other is to minimize its parallel execution time. Three candidate definitions are proposed for the first of these criteria, constituting a sequence of increasing optimality guarantees. Two of these are based on deordering plans, which means that ordering relations may only be removed, not added, while the third one uses reordering, where arbitrary modifications to the ordering are allowed. It is shown that only the weakest one of the three criteria is tractable to achieve, the other two being NP-hard and even difficult to approximate. Similarly, optimising the parallel execution time of a plan is studied both for deordering and reordering of plans. In the general case, both of these computations are NP-hard. However, it is shown that optimal deorderings can be computed in polynomial time...

Sapa is a domain-independent heuristic forward chaining planner that can handle durative actions, metric resource constraints, and deadline goals. It is designed to be capable of handling the multi-objective nature of metric temporal planning. Our technical contributions include (i) planning-graph based methods for deriving heuristics that are sensitive to both cost and makespan (ii) techniques for adjusting the heuristic estimates to take action interactions and metric resource limitations into account and (iii) a linear time greedy post-processing technique to improve execution flexibility of the solution plans. An implementation of Sapa using many of the techniques presented in this paper was one of the best domain independent planners for domains with metric and temporal constraints in the third International Planning Competition, held at AIPS-02. We describe the technical details of extracting the heuristics and present an empirical evaluation of the current implementation of Sapa. 1.