The Fourteenth International Conference on Automated Planning and Scheduling (ICAPS-04) was held in Canada in June of 2004. It covered the latest theoretical and empirical advances in planning and scheduling. The conference program consisted of tutorials, workshops, a doctoral consortium, and three days of technical paper presentations in a single plenary track, one day of which was jointly organized with the Ninth International Conference on Principles of Knowledge Representation and Reasoning. ICAPS-04 also hosted the International Planning Competition, including a classical track and a newly formed probabilistic track. This report describes the conference in more detail.

The solution is based on the replacement of standard modules of a program with more flexible computation elements that are called anytime algorithms (Dean and Boddy 1988; Horvitz 1989). In addition, the solution includes an offline compilation process and a run-time monitoring component that guarantee that the agent is performing the correct amount of thinking in a well-defined sense. Artificial agents must perform some real-time deliberation to solve such problems as path planning and task scheduling. An important aspect of intelligent behavior is the capability of agents to factor the cost of deliberation into the deliberation process. Two factors determine the cost of deliberation: (1) the resources consumed by the process, primarily computation time, and (2) constant change in the environment that might decrease the relevance of the outcome and, hence, reduce its value.

The 2003 International Conference on Automated Planning and Scheduling (ICAPS-03) was held 9 to 13 June 2003 in Trento, Italy. It was chaired by Enrico Giunchiglia (University of Genova), Nicola Muscettola (NASA Ames), and Dana Nau (University of Maryland). Piergiorgio Bertoli and Marco Benedetti (both from ITC-IRST) were the local chair and the workshop-tutorial coordination chair, respectively. It is the result of merging two highly successful biennial conferences: (1) the International Conference on AI Planning and Scheduling (AIPS) and (2) the European Conference on Planning (ECP)--which alternately occurred beginning in 1991. The ICAPS-03 technical program took place from 11 to 13 June 2003.

This article reports on an extensive survey and analysis of research work related to machine learning as it applies to automated planning over the past 30 years. Major research contributions are broadly characterized by learning method and then descriptive subcategories. Survey results reveal learning techniques that have extensively been applied and a number that have received scant attention. We extend the survey analysis to suggest promising avenues for future research in learning based on both previous experience and current needs in the planning community. Within the AI research community, machine learning is viewed as a potentially powerful means of endowing an agent with greater autonomy and flexibility, often compensating for the designer's incomplete knowledge of the world that the agent will face and incurring low overhead in terms of human oversight and control.

Planning--the ability to synthesize a course of action to achieve desired goals--is an important part of intelligent agency and has thus received significant attention within AI for more than 30 years. Work on efficient planning algorithms still continues to be a hot topic for research in AI and has led to several exciting developments in the past few years. This article provides a tutorial introduction to all the algorithms and approaches to the planning problem in AI. To fulfill this ambitious objective, I introduce a generalized approach to plan synthesis called refinement planning and show that in its various guises, refinement planning subsumes most of the algorithms that have been, or are being, developed. It is hoped that this unifying overview provides the reader with a brand-name-free appreciation of the essential issues in planning.

A prerequisite for intelligent behavior is the ability to reason about actions and their effects. This ability is the essence of the classical AI planning problem in which plans are constructed by reasoning about how available actions can be applied to achieve various goals. For this reasoning process to occur, the planner must be aware of its available actions, the situations in which they are applicable, and the changes affected in the world by their execution. Classical AI planners typically use a highlevel, symbolic representation of actions (for example, well-formed formulas from predicate calculus). Although this type of representational scheme is attractive from a computational standpoint, it cannot adequately represent the intricacies of a domain that includes complex actions, such as robotic assembly (consider, for example, that any geometric configuration of the robotic manipulator is a rather complex function of six joint angles).

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 staff scheduling problem is a critical problem in the call center (or, more generally, customer contact center) industry. Even the simplest variations of this problem are known to be NPcomplete (Garey and Johnson 1978). Although staff scheduling has long been an important operations research problem, scheduling has recently become an important component of an emerging class of business software applications known as workforce management software. The need for effective workforce management systems has been driven primarily by the recent, rapid growth of the call center--customer contact center industry, in which efficient deployment of human resources is of crucial, strategic importance. Traditionally, in this industry, staff scheduling has been performed using ad hoc methods and operations research techniques (Cleveland and Mayben 1997).

The Modified Antarctic Mapping Mission (MAMM) was conducted from September to November 2000 onboard RADARSAT. The mission plan consisted of more than 2400 synthetic aperture radar data acquisitions of Antarctica that achieved the scientific objectives and obeyed RADARSAT's resource and operational constraints. Mission planning is a time-and knowledge-intensive effort. It required over a workyear to manually develop a comparable plan for AMM-1, the precursor mission to MAMM. This article describes the design and use of the automated mission planning system for MAMM, which dramatically reduced mission-planning costs to just a few workweeks and enabled rapid generation of what-if scenarios for evaluating alternative mission designs.

FF. Readers interested in these The 1 sink can only be reached by following the edges labeled 1 from A and B; thus, the represented Boolean function (A, B) evaluates to true if and only if A and B are true. The characteristic function can be identified with the set itself. It seems worthwhile to spend some effort on finding a "good" encoding, which is where the preprocessing of The corresponding BDDs are illustrated in figure 2. Bin We were able to reformulate the initial and final situations as BDDs. As an end in itself, this representation does not help too much. We are interested in a sequence of actions (or transitions) that transforms an initial state into one that satisfies the goal condition.