The Second International Workshop on User Modeling was held March 30-April 1, 1990 in Honolulu, Hawaii. The general chairperson was Dr. Wolfgang Wahlster of the University of Saarbrucken; the program and local arrangements chairperson was Dr. David Chin of the University of Hawaii at Manoa. The workshop was sponsored by AAAI and the University of Hawaii, with AAAI providing eight travel stipends for students. An excellent response to the call for papers and participants resulted in 46 high quality submissions, of which 24 were selected for presentation and discussion led by invited commentators. Whereas the first user modeling workshop, held in Maria Laach, West Germany in 1986, focused on user modeling in natural language dialogue systems, the 1990 workshop covered a broader range of topics, including user modeling in tutoring systems and psychological foundations of user modeling.

SHOP's preconditions can include logical inferences, SHOP's expressive power can be used to create Here, we summarize the SHOP algorithm's primary SHOP algorithm is shown in figure 1. S is a state, T is a list of tasks, and D is the knowledge base (methods, operators, and Horn-clause axioms). As long as the procedure for inferring m's preconditions from S is a sound and complete inference procedure (such as Horn-clause theorem proving), the For example, the Horn clauses can include calls to attached procedures for numeric computations (for example, "distance(UofMD,BWI) 50" in the previous example), or (in some of the implementations) any other procedure calls defined by the user. In our experiments (Nau et al. 1999), SHOP generated SHOP's higher level of expressivity made PLAN and SHOP was not too different. We intend to make more optimizations in the near future. HICAP is shown in figure 4. HICAP (Aha and Breslow 1997).

We report on the staging of the first competition on knowledge engineering for AI planning and scheduling systems, held in Monterey, California, in colocation with the ICAPS 2005 conference. The background and motivation is discussed, together with the relationship of this new competition with the current international planning competition. We report on the new competition's format, its outcome, and the benefits we hope it will bring to the research area. Further, the IPC has facilitated the sharing of benchmark domain models, tasks, and planning tools through the use of PDDL. However, the narrow focus and limiting assumptions of the IPC are controversial--it encourages rapid development, but in the narrow area of fully autonomous plan generation.

To construct a perpetual self-aware cognitive agent that can continuously operate with independence, an introspective machine must be produced. To assemble such an agent, it is necessary to perform a full integration of cognition (planning, understanding, and learning) and metacognition (control and monitoring of cognition) with intelligent behaviors. The failure to do this completely is why similar, more limited efforts have not succeeded in the past. I outline some key computational requirements of metacognition by describing a multistrategy learning system called Meta-AQUA and then discuss an integration of Meta-AQUA with a nonlinear state-space planning agent. I show how the resultant system, INTRO, can independently generate its own goals, and I relate this work to the general issue of self-awareness by machine.

The MAPGEN system represents a successful mission infusion of mixed-initiative planning technology. MAPGEN was deployed as a mission-critical component of the ground operations system for the Mars Exploration Rover mission. Each day, the ground-planning personnel employ MAPGEN to collaboratively plan the activities of the Spirit and Opportunity rovers, with the objective of achieving as much science as possible while ensuring rover safety and keeping within the limitations of the rovers' resources. The Mars Exploration Rover mission has now been operating for more than two years, and MAPGEN continues to be employed for activity plan generation for the Spirit and Opportunity rovers. During the multiyear deployment effort and subsequent mission operations experience, we have learned valuable lessons regarding application of mixed-initiative planning technology to mission operations.

Mixed-initiative planning systems attempt to integrate human and AI planners so that the synthesis results in high-quality plans. In the AI community, the dominant model of planning is search. In state-space planning, search consists of backward and forward chaining through the effects and preconditions of operator representations. Although search is an acceptable mechanism to use in performing automated planning, we present an alternative model to present to the user at the interface of a mixed-initiative planning assistant. That is, we propose to model planning as a goal-manipulation task.

Mixed-initiative assistants are agents that interact seamlessly with humans to extend their problem-solving capabilities or provide new capabilities. Developing such agents requires the synergistic integration of many areas of AI, including knowledge representation, problem solving and planning, knowledge acquisition and learning, multiagent systems, discourse theory, and human-computer interaction. This paper introduces seven aspects of mixed-initiative reasoning (task, control, awareness, communication, personalization, architecture, and evaluation) and discusses them in the context of several state-of-the-art mixed-initiative assistants. The goal is to provide a framework for understanding and comparing existing mixed-initiative assistants and for developing general design principles and methods. Mixed initiative assumes an efficient, natural interleaving of contributions by users and automated agents that is determined by their relative knowledge and skills and the problem-solving ...

The late 1990s saw a huge upswing in the demand for supply-chain management software. The rapid commercial adoption of these tools brought into sharp relief the complexity of automating supply-chain planning and optimization.

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 collocated 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.

Automated planning is a branch of AI that addresses the problem of generating a set of actions to achieve a specified goal state, given an initial state of the world. It is an active area of research that is central to the development of intelligent agents and au - tonomous robots. In many real-world applications, a multitude of valid plans exist, and a user distinguishes plans of high quality by how well they adhere to the user's preferences. To generate such high-quality plans automatically, a planning system must provide a means of specifying the user's preferences with respect to the planning task, as well as a means of generating plans that ideally optimize these preferences. In the last few years, there has been significant research in the area of planning with preferences.