The following is a synopsis of the findings of the first AAAI Workshop on AI Applications to Battle Management held at the University of Washington, 16 July 1987. The workshop organizer, Pete Bonasso, sent a point paper to a number of invited presenters giving his opinion of what AI could and could not do for battle management. This paper served as a focus for the workshop presentations and discussions and was augmented by the workshop presentations; it can also serve as a roadmap of topics for future workshops. AI can provide battle management with such capabilities as sensor data fusion and adaptive simulations. Also, several key needs in battle management will be AI research topics for years to come, such as understanding free text and inferencing in real time. Finally, there are several areas -- cooperating systems and terrain reasoning, for example -- where, given some impetus, AI might be able to provide help in the near future.

Sometimes less ambiguous, are extremely complex.

There was a six-way tie were losses to International Master's, for first with 5.5 points between: and there were two draws against International Grandmaster Mikhail players rated over 2500, (one an International Tal (a former world champion), International Master). Grandmaster Sergey Kudrin, From previous tournaments rated FIDE Master Michael Brooks, International by FIDE, the international chess federation, Master James Rizzitano, International Hitech has achieved a performance Master Calvin Blocker, and worthy of a FIDE rating. However, International Grandmaster Leonid at present FIDE is declining to Shamkovich. Hitech has met every points were: National Master Hitech, qualification but one for achieving a International Grandmaster Maxim rating--it is not a human. If Hitech Dlugy, International Grandmaster were eligible for a rating, its FIDE rating Walter Browne, International Grandmaster would be 2350, which would qualify Arthur Bisguier, and nine others.

Our guest editor is Avi Kak, of Purdue University. We also round out the issue with the final installment of Steven Frank's Swartout, on an AAAIsponsored Planning Workshop, held last year. "open-ended" (i.e., almost any aspect of the experienced world might be Book reviews should be submitted to the Book Review Editor, Bruce D'Ambrosio, Computer Science Department, Oregon State University, Corvallis, OR 97331 (503) 754.4466 Advertising rates and media kits are available upon request from AI Magazine, 445 Burgess Drive, Menlo Park, CA 94025 Telephone (415) 328.3123

Moravec has interests in computer animation and three dimensional graphics. He has produced illustrations and films presenting progress in the other work and published in the areas of mobile robots, computer vision, robots and the future, orbital skyhooks, switching networks, and three dimensional Keith M. Andress, coauthor of "Evidence Accumulation and Flow of Control in graphics a Hierarchical Spatial Reasoning System, " is a research associate in the Robot Vision Lab at Purdue University His research interests are in formalisms for Gudula Retz-Schmidt received her accumulation of evidence, expert systems, and computer vision. He can be Master degree (Dipl.-Inform) Practitioners Should Know about the Law. Part Two" is an attorney practicing Benjamin J. Kuipers, coauthor of William Swartout, editor of "Summary with Nutter, McClennen & Fish, "Navigation and Mapping in Large-Report on DARPA Santa Cruz One International Place, Boston, Massachusetts Scale Space" is an associate professor Workshop on Planning" is a senior 02210-2699. His research Framework for Representing and Reasoning research interests include qualitative interests include explanation of about Three-Dimensional reasoning about physical mechanisms expert systems, natural language generation, Objects for Vision" is group leader of and qualitative representations and expert system architectures, the Sensory Intelligence Group in the learning strategies for spatial knowledge.

It was felt that the AIM Szolovits responding, and "Uncertainty approaches, augmenting explanations

In a large-scale space, structure is at a significantly larger scale than the observations available at an instant. To learn the structure of a large-scale space from observations, the observer must build a cognitive map of the environment by integrating observations over an extended period of time, inferring spatial structure from perceptions and the effects of actions. The cognitive map representation of large-scale space must account for a mapping, or learning structure from observations, and navigation, or creating and executing a plan to travel from one place to another. Approaches to date tend to be fragile either because they don't build maps; or because they assume nonlocal observations, such as those available in preexisting maps or global coordinate systems, including active landmark beacons and geo-locating satellites. We propose that robust navigation and mapping systems for large-scale space can be developed by adhering to a natural, four-level semantic hierarchy of descriptions for representation, planning, and execution of plans in large-scale space. The four levels are sensorimotor interaction, procedural behaviors, topological mapping, and metric mapping. Effective systems represent the environment, relative to sensors, at all four levels and formulate robust system behavior by moving flexibly between representational levels at run time. We demonstrate our claims in three implemented models: Tour, the Qualnav system simulator, and the NX robot.

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, identify locations (by correlating maps), identify well-known and insufficiently sensed terrain, and perhaps identify objects by shape. 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 of its motion and occasional changes in the terrain.

In this article, principles involving the intrinsic, deictic, and extrinsic use of spatial prepositions are examined from linguistic, psychological, and AI approaches. First, I define some important terms. Second, those prepositions which permit intrinsic, deictic, and extrinsic use are specified. Third, I examine how the frame of reference is determined for all three cases. Fourth, I look at ambiguities in the use of prepositions and how they can be resolved. Finally, I introduce the natural language dialog system CITYTOUR, which can cope with the intrinsic, deictic, and extrinsic use of spatial prepositions, and compare it with the approaches dealt with in the previous sections as well as to some other AI systems.

A fundamental goal of computer vision is the development of systems capable of carrying out scene interpretation while taking into account all the available knowledge. In this article, we focus on how the interpretation task can be aided by the expected scene information (such as map knowledge), which, in most cases, would not be in registration with the perceived scene. The proposed approach is applicable to the interpretation of scenes with three-dimensional structures as long as it is possible to generate the equivalent two-dimensional orthogonal or perspective projections of the structures in the expected scene. The system is implemented as a two-panel, six-level blackboard and uses the Dempster-Shafer formalism to accomplish inexact reasoning in a hierarchical space. Inexact reasoning involves exploiting, at different levels of abstraction, any internal geometric consistencies in the data and between the data and the expected scene. As they are discovered, these consistencies are used to update the system's belief in associating a data element with a particular entity from the expected scene.