As in past years, papers were solicited in two categories: (1) deployed applications and (2) emerging applications and technologies. Deployed applications are systems that have been in use for at least several months by individuals or organizations other than their developers, have measurable benefits, and incorporate AI technologies. Emerging applications are technologies and systems that are close to deployment and clearly show an innovative implementation of AI technologies. All these case studies are of value not only to other application developers looking for guidance in applying various techniques to their own applications but also to researchers who need to understand the myriad of technical challenges provided by real-world problems. At IAAI-2001, five deployed applications and seven emerging application papers were presented.

As in past years, papers were solicited in two categories: (1) deployed applications and (2) emerging applications and technologies. Deployed application papers describe systems that have been in use for at least several months by individuals or organizations other than their developers, have measurable benefits, and incorporate AI technologies. Emerging applications are technologies and systems that are close to deployment and clearly show an innovative implementation of AI technologies. These papers are of value not only to other application developers looking for guidance in applying various techniques to their own applications but also to researchers who need to understand the unique technical challenges provided by real-world problems. For IAAI-2002, we received 54 submissions, containing a wealth of outstanding applications and emerging technology papers (15 deployed and 39 emerging).

This case study article describes the iterative design process of an AIbased mixed-initiative calendaring tool with embedded artificial intelligence. We establish the specific types of assistance in which the target user population expressed interest, and we highlight our findings regarding the scheduling practices and the reminding preferences of these users. These findings motivated the redesign and enhancement of our intelligent system. Lessons learned from the study--namely, that AI systems must be usable to gain widespread adoption and retention and that simple problems that perhaps do not necessitate complex AIbased solutions should not go unattended merely because of their inherent simplicity--conclude the article, along with a discussion of the importance of the iterative design process for any user adaptive system. We are working within the infrastructure of a general-purpose, computerized office assistant named CALO (Myers et al. 2007).

This article surveys previous work on combining planning techniques with expressive representations of knowledge in description logics to reason about tasks, plans, and goals. Description logics can reason about the logical definition of a class and automatically infer class-subclass subsumption relations as well as classify instances into classes based on their definitions. Descriptions of actions, plans, and goals can be exploited during plan generation, plan recognition, or plan evaluation. These techniques should be of interest to planning practitioners working on knowledge-rich application domains. Another emerging use of these techniques is the semantic web, where current ontology languages based on description logics need to be extended to reason about goals and capabilities for web services and agents.

This is a summary of the Workshop on Planning that was sponsored by the Defense Advanced Research Project Agency and held in Santa Cruz, California, on October 21-23, 1987. The purpose of this workshop was to identify and explore new directions for research in planning. For the purposes of this article, a planner is a program that controls one or more devices capable of carrying out actions in the real world in order to achieve some definite purpose. A strategic planner is capable of anticihe workshop was organized into five sessions. Each session was intended to examine some aspect of planning research or point directions toward future work.

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. One motivation for automated-planning research is theoretical: planning is an important component of rational behavior--so if one objective of artificial intelligence is to grasp the computational aspects of intelligence, then certainly planning plays a critical role. Another motivation is very practical: plans are needed in many different fields of human endeavor, and in some cases it is desirable to create these plans automatically.

First, besides dealing with time constraints and other constraints of the job shop domain (for example, equipment constraints), crew planners must also deal with space constraints to prevent space discontinuities in duties, positioning crew where they are needed, whether as passengers in trains or other transportation means. Second, crew planners must also deal with complex train frequencies, such as week frequencies (for example, a train might only run on weekends), year periods (for example, only during summer), and special days (for example, on holidays and days before holidays), which put additional constraints on the combinations of tasks. These two aspects are critical to the quality of the final schedules and the efficiency of the scheduling process, requiring abstraction techniques to be used extensively. Third, the work periods of crew do not have fixed times, as shifts in industry, but can slide during the day to accommodate the irregularity of the train operation, although subject to constraints. The resource-sliding dynamics make it difficult to analyze activity demand and resource contention as is usually done in the job shop domain (Sadeh and Fox 1991).

With the exception of plan repair, important topics related to the use of plans (robust execution, reactivity, monitoring, evaluation) have received significantly less consideration. In realistic domains, however, plan generation is only a small component of the overall package. In particular, plans must be updated in response to new information and requirements in a timely fashion to ensure that they remain viable and relevant. Plan execution involves more than blind adherence to previously generated plans. Rather, run-time decisions are made to adapt, initiate, or abandon plans and activities in response to current considerations within the operating environment.

DSIPE supports a human planner. Although their requirements overlap for this mission, they each have independent goals (other missions to be performed or supported), capabilities, and resources. We extended SIPE-2's internal plan representation Throughout the planning process, each planning system monitors the local cell's planning activity for constraints and subgoals that might be relevant to other planning cells and notifies the cells of this information. For example, the naval planning cell might notify the Marine Corps planner that a particular landing area will be swept of mines by a specified time. Currently, the only constraints that are monitored in this way are the postconditions.

Introduction One of the major deterrents to productivity in industry today is the inability to effectively manage and control production. The problem is particularly acute in job shop environments where plant operation is routinely characterized by high work-in-process (WIP) inventories, tardy orders, poor resource utilization, and other shop floor inefficiencies. Perhaps the single most significant obstacle to improved factory performance is the complexity associated with constructing and maintaining good production schedules. Good schedules must reflect both the full detail of the operating environment and the influence of a conflicting set of preferences that range from global organizational objectives to specific operational idiosyncrasies. Existing computer-based techniques for production scheduling are capable of incorporating only a small fraction of this scheduling knowledge and, as a result, typically produce schedules that bear little resemblance to the actual state of the ...