The 1998 Planning Competition at the AI Planning Systems Conference was the first of its kind. Its goal was to create planning domains that a wide variety of planning researchers could agree on to make comparison among planners more meaningful, measure overall progress in the field, and set up a framework for long-term creation of a repository of problems in a standard notation. A rules committee for the competition was created in 1997 and had long discussions on how the contest should go. One result of these discussions was the pddl notation for planning domains. This notation was used to set up a set of planning problems and get a modest problem repository started.

The Third International Conference on Artificial Intelligence Planning Systems (AIPS-96) was held in Edinburgh, Scotland, from 29 to 31 May 1996. The main gathering of researchers in AI and planning and scheduling, the conference promoted the practical applications of planning technologies. Details of the conference papers and sessions are provided as well as information on the Defense Advanced Research Projects Agency-Rome Laboratory Planning Initiative. Previous conferences were held at the University of Maryland in June 1992 (AIPS-92), organized by Jim Hendler and Drew McDermott, and the University of Chicago in June 1994 (AIPS-94), organized by Kristian Hammond. The generation of plans and related fields, such as scheduling, resource allocation, and reasoning about action, have a long research tradition in AI.

The National Aeronautics and Space Administration (NASA) is being challenged to perform more frequent and intensive space-exploration missions at greatly reduced cost. Nowhere is this challenge more acute than among robotic planetary exploration missions that the Jet Propulsion Laboratory (JPL) conducts for NASA. This article describes recent and ongoing work on spacecraft autonomy and ground systems that builds on a legacy of existing success at JPL applying AI techniques to challenging computational problems in planning and scheduling, real-time monitoring and control, scientific data analysis, and design automation. I research and technology development reached critical mass at the Jet Propulsion Laboratory (JPL) about five years ago. In the last three years, the effort has begun to bear fruit in the form of numerous JPL and National Aeronautics and Space Administration (NASA) applications of AI technology in the areas of planning and scheduling, real-time monitoring and control, scientific data analysis, and design automation.

Intelligent Scheduling is a system-oriented book on scheduling systems. Each chapter describes a scheduling system in terms of the particular scheduling problems being addressed, design assumptions, and the overall paradigm being used. The book is divided into two sections: (1) scheduling methodologies and (2) application case studies. The methodology chapters focus on research systems and scheduling techniques. The application chapters focus on fielded embedded scheduling systems and describe difficulties and lessons learned.

We report on the staging of the third competition on knowledge engineering for AI planning and scheduling systems, held during ICAPS-09 at Thessaloniki, Greece, in September 2009. We give an overview of how the competition has developed since its first run in 2005 and its relationship with the AI planning field. This run of the competition focused on translators that, when input with some formal description in an application-area-specific language, output solver-ready domain models. Despite a fairly narrow focus within knowledge engineering, seven teams took part in what turned out to be a very interesting and successful competition. The International Competition on Knowledge Engineering for Planning and Scheduling (ICKEPS) has been running since 2005 as a biannual event promoting the development and importance of the use of knowledge engineering methods and techniques within planning and scheduling.

This conference brought together researchers working in all aspects of problems in planning, scheduling, planning and learning, and plan execution for dealing with complex problems. The format of the conference included paper presentations, invited speakers, panel discussions, workshops, and a planning competition. The conference was cochaired by Steve Chien of the Jet Propulsion Laboratory (JPL) at the California Institute of Technology, Subbarao Kambhampati of Arizona State University, and Craig Knoblock of the University of Southern California Information Sciences Institute, with the proceedings published by AAAI Press (Chien, Kambhampati, and Knoblock 2000). The three workshops were "Analyzing and Exploiting Domain Knowledge for Efficient Planning," chaired by Maria Fox from University of Durham; "Decision-Theoretic Planning," chaired by Richard Goodwin from IBM's T. J. Watson Research Center and Sven Koenig from Georgia Institute of Technology; and "Model-Theoretic Approaches to Planning" by Paolo Traverso from The invited speakers at the conference presented some of their latest research and ideas on intelligent planning and execution: Drew McDermott from Yale University gave the first talk, entitled "Bottom-Up Knowledge Representation," and David Smith from The Fifth International Conference on Artificial Intelligence Planning and Scheduling (AIPS2000) was held on 14-17 April 2000 at Breckenridge, Colorado; it was colocated with the Seventh International Conference on Principles of Knowledge Representation and Reasoning (KR2000). This conference brought together researchers working in all aspects of problems in planning, scheduling, planning and learning, and plan execution for dealing with complex problems.

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.