Automated Planning is an old area of AI that focuses on the development of techniques for finding a plan that achieves a given goal from a given set of initial states as quickly as possible. In most real-world applications, users of planning systems have preferences over the multitude of plans that achieve a given goal. On the other hand, we have seen the development of planning techniques that aim at finding high-quality plans quickly, exploiting some of the ideas developed for classical planning. In this paper we review the latest developments in automated preference-based planning.

Automated Planning is an old area of AI that focuses on the development of techniques for finding a plan that achieves a given goal from a given set of initial states as quickly as possible. In most real-world applications, users of planning systems have preferences over the multitude of plans that achieve a given goal. These preferences allow to distinguish plans that are more desirable from those that are less desirable. Planning systems should therefore be able to construct high-quality plans, or at the very least they should be able to build plans that have a reasonably good quality given the resources available.In the last few years we have seen a significant amount of research that has focused on developing rich and compelling languages for expressing preferences over plans. On the other hand, we have seen the development of planning techniques that aim at finding high-quality plans quickly, exploiting some of the ideas developed for classical planning. In this paper we review the latest developments in automated preference-based planning. We also review various approaches for preference representation, and the main practical approaches developed so far.

IAAI-09) will be held July scholarship applications to AAAI by 2009. No late submissions will be accepted.

The Seventeenth International Conference on Automated Planning and Scheduling (ICAPS-07) was held in Providence, Rhode Island in September 2007. It covered the latest theoretical and practical advances in planning and scheduling. The conference was co-located with the Thirteenth International Conference on Principles and Practice of Constraint Programming (CP-07). ICAPS-07 also hosted the second edition of the International Competition on Knowledge Engineering for Planning and Scheduling.

The Seventeenth International Conference on Automated Planning and Scheduling (ICAPS-07) was held in Providence, Rhode Island in September 2007. It covered the latest theoretical and practical advances in planning and scheduling. The conference was co-located with the Thirteenth International Conference on Principles and Practice of Constraint Programming (CP-07). The program consisted of tutorials, workshops, system demonstrations, a doctoral consortium, and three days of technical presentations mostly in parallel sessions. ICAPS-07 also hosted the second edition of the International Competition on Knowledge Engineering for Planning and Scheduling. This report describes the conference in more detail.

In fact, DARPA, which funded the project, ways. Elves) (Scerri, Pynadath, and Tambe 2002; Finally, we will present some lessons Pynadath and Tambe 2003) and required learned and recent research that was motivated detailed information about the calendars by our experiences in deploying the of people using the system. Thus, we decided to deploy a new application of the Electric The Travel Elves introduced two major Elves, called the Travel Elves. This application advantages over traditional approaches to appeared to be ideal for wider deployment travel planning. First, the Travel Elves provided since it could be hosted entirely outside an interactive approach to making an organization and communication travel plans in which all of the data could be performed over wireless devices, required to make informed choices is such as cellular telephones. For example, when The mission of the Travel Elves (Ambite deciding whether to park at the airport or et al. 2002, Knoblock 2004) was to facilitate take a taxi, the system compares the cost planning a trip and to ensure that the of parking and the cost of a taxi given other resulting travel plan would execute selections, such as the airport, the specific smoothly. Initial deployment of the Travel parking lot, and the starting location Elves at DARPA went smoothly.

Technically the underlying optimization development of online commerce forced problem is either NP or P-space businesses to question the week-plus supply-chain complete (depending on the details of the planning cycles that had been domain). Furthermore, the problem mixes the norm. Finally, the year 2000 (Y2K) a dozen or so classic optimization problems problem caused an across-the-board from AI and operations research (OR), replacement of enterprise software, allowing and much of the expected savings from many businesses to update their global supply-chain optimization are lost if approach to supply-chain planning. The end result of all of these factors was This article describes our experience a huge upswing in demand for supplychain from four years of solving supply-chain planning tools from i2 Technologies planning and optimization problems and other vendors. When I joined i2 in across industries, and some of the lessons 1996 as optimization architect, the company we learned.

Scheduling problems are generally NP-hard combinatorial problems, and a lot of research has been done to solve these problems heuristically. However, most of the previous approaches are problem-specific and research into the development of a general scheduling algorithm is still in its infancy. Mimicking the natural evolutionary process of the survival of the fittest, Genetic Algorithms (GAs) have attracted much attention in solving difficult scheduling problems in recent years. Some obstacles exist when using GAs: there is no canonical mechanism to deal with constraints, which are commonly met in most real-world scheduling problems, and small changes to a solution are difficult. To overcome both difficulties, indirect approaches have been presented (in [1] and [2]) for nurse scheduling and driver scheduling, where GAs are used by mapping the solution space, and separate decoding routines then build solutions to the original problem.

This article provides an overview of the nature and role of autonomy for space exploration, with a bias in focus towards describing the relevance of AI technologies. It explores the range of autonomous behavior that is relevant and useful in space exploration and illustrates the range of possible behaviors by presenting four case studies in space-exploration systems, each differing from the others in the degree of autonomy exemplified. Three core requirements are defined for autonomous space systems, and the architectures for integrating capabilities into an autonomous system are described. The article concludes with a discussion of the challenges that are faced currently in developing and deploying autonomy technologies for space.

Automated planning technology has become mature enough to be useful in applications that range from game-playing to control of space vehicles. In this article, Dana Nau discusses where automated-planning research has been, where it is likely to go, where he thinks it should go, and some major challenges in getting there. The article is an updated version of Nau's invited talk at AAAI-05 in Pittsburgh, Pennsylvania.