This research was sponsored in part by the Office of Naval Research under contract NOOO14.80-C-0732 and the National Science Foundation under grant hZCS79-06543 IUNIX is trademark of Bell Laboratories These include the following: 1. A robust language analyzer, which almost never has a "hard" failure and which has the ability to handle most elliptical constructions in context 2 A context and memory mechanism that determines the focus of attention and helps with lexical and syntactic disambiguation, and with some aspects of pronominal reference. While some of the components of the system are experimental in nature: the basic features of UC provide a usable device to obtain information about UNIX. In addition, THE AI,MAGAZINE Spring 1984 29 it is straightforward to extend UC's knowledge base to cover UNIX with which UC is not currently familiar. How do I delete a file?

Planning domains often feature subproblems such as route planning and resource handling. Using static domain analysis techniques, we have been able to identify certain commonly occurring subproblems within planning domains, making it possible to abstract these subproblems from the overall goals of the planner and deploy specialized technology to handle them in a way integrated with the broader planning activities. Although such strategies can be impressive when applied to toy domains, they cannot address highly structured problem domains effectively. However, when knowledge-sparse approaches are supplemented by domain knowledge, they can perform impressively (Bacchus and Kabanza 2000) at the cost of an increased representation burden on the domain designer.

A numeric representation of uncertain and incomplete sensor knowledge called certainty grids was used successfully in several recent mobile robot control programs developed at the Carnegie-Mellon University Mobile Robot Laboratory (MRL) Certainty grids have proven to be a powerful and efficient unifying solution for sensor fusion, motion planning, landmark identification, and many other central problems MRL had good early success with ad hoc formulas for updating grid cells with new information. A new Bayesian statistical foundation for the operations promises further improvement MRL proposes to build a software framework running on processors onboard the new Uranus mobile robot that will maintain a probabilistic, geometric map of the robot's surroundings as it moves, The certainty grid representation will allow this map to be incrementally updated in a uniform way based on information coming from various sources, including sonar, stereo vision, proximity, and contact sensors The approach can correctly model the fuzziness of each reading and, at the same time, combine multiple measurements to produce sharper map features; it can also deal correctly with uncertainties in the robot's motion The map will be used by planning programs to choose clear paths, The certainty grid representation can be extended in the time dimension and used to detect and track moving objects Even the simplest versions of the idea allow us to fairly straightforwardly program the robot for tasks that have hitherto been out of reach MRL looks forward to a program that can explore a region and return to its starting place, using map "snapshots" from its outbound journey to find its way back, even in the presence of disturbances Objects have been modeled by polygons and polyhedra or bounded by curved surfaces. Free space has been partitioned into Vornoi regions or, heuristically, free corridors. Traditionally, the models have been hard edged; positional uncertainty, if considered at all, was used in just a few special places in the algorithms, expressed as a Gaussian spread. Partly, this oversimplification of uncertainty information is the result of analytic difficulty in manipulating interacting uncertainties, especially if the distributions are not Gaussian.

The concatenation of these reports forms the body of this article. Abstract Based on the experience in manufacturing production scheduling problems which the AI community has amassed over the last ten years, a workshop was held to provide a forum for discussion of the issues encountered in the design of AIbased scheduling systems. Several topics were addressed including: the relative virtues of expert system, deep method, and interactive approaches, the balance between predictive and reactive components in a scheduling system, the maintenance of convenient scheduling descriptions, the application of the ideas of chaos theory to scheduling, the state of the art in schedulers which learn, and the practicality and desirability of a set of benchmark scheduling problems. This article expands on these issues, abstracts the papers which were presented, and summarizes the lengthy discussions that took place. Since its first formal business meeting in August of 1988, the American Association for Artificial Intelligence Special Interest Group in Manufacturing (SIGMAN) has held a number of workshops, three of which have been concerned with the application of AI techniques to the problem of manufacturing scheduling.

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.