Drew McDermott Research on planning for robots is in such a state of flux that there is disagreement about what planning is and whether it is necessary. We can take planning to be the optimization and debugging of a robot's program by reasoning about possible courses of execution. It is necessary to the extent that fragments of robot programs are combined at run time. There are several strands of research in the field; I survey six: (1) attempts to avoid planning; (2) the design of flexible plan notations; (3) theories of time-constrained planning; (4) planning by projecting and repairing faulty plans; (5) motion planning; and (6) the learning of optimal behaviors from reinforcements. More research is needed on formal semantics for robot plans.

To date, using Tesla's trip planning tool has meant sitting inside your electric car while you map a route that takes you past charging stations. That doesn't make much sense if you're gearing up for vacation, does it? There's now a better way: Tesla has launched a web version of its trip planner to use while you're still sitting at your desk. It's not as fleshed out as the in-car version, but it can tell you where you'll need to charge and how long you need to drive based on both the route and the particular Tesla you're driving. You could see fewer stops with a Model S P100D than you would with a Model X 75D, for instance. Convenience is clearly the main goal for the web planner, but it's also helpful if you're a prospective buyer.

There's More to Life Than Making Plans For many years, research in AI plan generation was governed by a number of strong, simplifying assumptions: The planning agent is omniscient, its actions are deterministic and instantaneous, its goals are fixed and categorical, and its environment is static. More recently, researchers have developed expanded planning algorithms that are not predicated on such assumptions, but changing the way in which plans are formed is only part of what is required when the classical assumptions are abandoned. The demands of dynamic, uncertain environments mean that in addition to being able to form plans--even probabilistic, uncertain plans--agents must be able to effectively manage their plans. In this article, which is based on a talk given at the 1998 AAAI Fall Symposium on Distributed, Continual Planning, we first identify reasoning tasks that are involved in plan management, including commitment management, environment monitoring, alternative assessment, plan elaboration, metalevel control, and coordination with other agents. We next survey approaches we have developed to many of these tasks and discuss a plan-management system we are building to ground our theoretical work, by providing us with a platform for integrating our techniques and exploring their value in a realistic problem.

The IJCAI-09 Workshop on Learning Structural Knowledge from Observations (STRUCK-09) took place as part of the International Joint Conference on Artificial Intelligence (IJCAI-09) on July 12 in Pasadena, California. The workshop program included paper presentations, discussion sessions about those papers, group discussions about two selected topics, and a joint discussion. As a result, many cognitive architectures use structural models to represent relations between knowledge of different complexity. Structural modeling has led to a number of representation and reasoning formalisms including frames, schemas, abstractions, hierarchical task networks (HTNs), and goal graphs among others. These formalisms have in common the use of certain kinds of constructs (for example, objects, goals, skills, and tasks) that represent knowledge of varying degrees of complexity and that are connected through structural relations.

The Find-Life-on-Mars event of the 1997 American Association for Artificial Intelligence Mobile Robot Competition and Exhibition featured robots trying to find and collect stationary and moving colored objects in an arena littered with real rocks. The 2-day event had 11 entries participating in both single-robot and multirobot categories, both with and without manipulators. During the event, many of the robots successfully demonstrated object recognition, obstacle avoidance, exploration, and the collection and depositing of objects. The general concept was to have the robots locate, collect, and deliver a variety of "life forms," including both stationary and moving objects. Technically, the event was designed to highlight mobile manipulation, object recognition, exploration, and obstacle avoidance in a relatively unstructured environment.

Although researchers have studied planning since the early days of AI, recent developments have revolutionized the field. Furthermore, work on propositional planning is closely related to the algorithms used in the autonomous controller for the National Aeronautics and Space Administration (NASA) Deep Space One spacecraft, launched in October 1998. As a result, our understanding of interleaved planning and execution has advanced as well as the speed with which we can solve classical planning problems. The goal of this survey is to explain these recent advances and suggest new directions for research. Because this article requires minimal AI background (for example, simple logic and basic search algorithms), it's suitable for a wide audience, but my treatment is not exhaustive because I don't have the space to discuss every active topic of planning research.

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.