The capabilities for representing and reasoning about three-dimensional (3-D) objects are essential for knowledge-based, 3-D photointerpretation systems that combine domain knowledge with image processing, as demonstrated by 3- D Mosaic and ACRONYM. Three-dimensional representation of objects is necessary for many additional applications, such as robot navigation and 3-D change detection. Geometric reasoning is especially important because geometric relationships between object parts are a rich source of domain knowledge. A practical framework for geometric representation and reasoning must incorporate projections between a two-dimensional (2-D) image and a 3-D scene, shape and surface properties of objects, and geometric and topological relationships between objects. In addition, it should allow easy modification and extension of the system's domain knowledge and be flexible enough to organize its reasoning efficiently to take advantage of the current available knowledge. We are developing such a framework -- the Frame-based Object Recognition and Modeling (3-D FORM) System. This system uses frames to represent objects such as buildings and walls, geometric features such as lines and planes, and geometric relationships such as parallel lines. Active procedures attached to the frames dynamically compute values as needed. Because the order of processing is controlled largely by the order of slot access, the system performs both top-down and bottom-up reasoning, depending on the current available knowledge. The FORM system is being implemented with the Carnegie-Mellon University-built Framekit tool in Common Lisp (Carbonell and Joseph 1986). To date, it has been applied to two types of geometric reasoning problems: interpreting 3-D wire frame data and solving sets of geometric constraints.

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.

Sometimes less ambiguous, are extremely complex.

Real-time domains present a new and challenging environment for the application of knowledge-based problem-solving techniques. However, a substantial amount of research is still needed to solve many difficult problems before real-time expert systems can enhance current monitoring and control systems. In this article, we examine how the real-time problem domain is significantly different from those domains which have traditionally been solved by expert systems. We conduct a survey on the current state of the art in applying knowledge-based systems to real-time problems and describe the key issues that are pertinent in a real-time domain. The survey is divided into three areas: applications, tools, and theoretic issues. From the results of the survey, we identify a set of real-time research issues that have yet to be solved and point out limitations of current tools for real-time problems. Finally, we propose a set of requirements that a real-time knowledge-based system must satisfy.

Letters to the editor on the lack of a central index to the field's published works and the fact that many original works are not published in journals; praise for Letovsky article -- stimulating and amusing. felt subsequent letters to editors were full of bombastic indignation; criticism of Kasday letter about it and Bob Engelmore's weak support of the article; dualism in regards to Letovsky letter; and a reply to criticism by Letovsky, acknowledging diaristic form.

Knowledge-Based Backtracking," is a principal researcher for the Advanced Technology Center, Boeing Computer Services, P.O. Jackson Y. Read, coauthor of "Real-Time Knowledge-Based Systems," is a senior analyst and associate investigator of the independent research project on real-time knowledge-based Jack Breese, who reviewed The Principles and Applications of Decision Analysis, systems at Lockheed Artificial Intelligence is with Rockwell, 444 High Street, Palo Alto, California 94301. Preston A. Cox, coauthor of "Real-Time Knowledge-Based Systems," is a scientific programmer specialist for Lockheed's Space System Division in Sunnyvale, Patrick Saint-Dizier is chairman of California. James L. Schmidt, coauthor of "Real- " is a research computer scientist Bryan M. Kramer, author of the Time Knowledge-Based Systems," is a in the Department of Computer review of Expert Systems, is affiliated scientific programmer and associate Science, Carnegie-Mellon University, with Xerox Canada, Inc., 5650 Yonge investigator of the independent Pittsburgh, Pennsylvania 15213 Street, North York, Ontario M2M research project on real-time knowledge-based 4G7, Canada. Intelligence Center, 2710 Sand Practitioners Should Know about the Thomas J. Laffey, coauthor of "Real-Hill Road, Menlo Park, California Law," is an attorney practicing with Time Knowledge-Based Systems," is a 94025. Nutter, McClennen & Fish, One research scientist and the principal International Place, Boston, Massachusetts investigator of the independent Jeffrey Stout is on the research staff of 02210-2699. She coedited the research Victor Lesser, coauthor of "Approximate in progress, "New Mexico State University's Processing in Real-Time Problem Yorick Wilks is the director of the Computing Research Laboratory."

This review examines two important works in decision analysis: Howard and Matheson's The Principles and Applications of Decision Analysis, and Winterfeldt and Edwards's "Decision Analysis and Behavioral Research.

This book is a collection of research papers covering a range of topics in expert systems and knowledge engineering.

Johnson-Laird In a field choked with seemingly impenetrable jargon, Quick and thorough. Philip Johnson-Laird has done the impossible: written a By mixing forward and backward chaining, goal search book about how the mind works that requires no advance time can be shortenedramatically And, using GURU's knowledge of artificial intelligence, neurophysiology, or multiple rule firing capabilityou can refire rules psychology, providing the single best introduction to cognitive as values change GURU also comes equipped with science available. "Philip Johnson-Laird has that rare gift of being a cognitive seamlessly integrated 4th generation decision support scientist of the first order, yet he addresses himself to capabilitiesuch as data base, spreadsheet, and the deep classical issues in psychology, in the philosophy report generator

An informal workshop on concurrent logic programming, metaprogramming, and open systems was held at Xerox Palo Alto Research Center (PARC) on 8-9 September 1987 with support from the Association for the Advancement of Artificial Intelligence. The 50 workshop participants came from the Japanese Fifth Generation Project (ICOT), the Weizmann Institute of Sci-ence in Israel, Imperial College in London, the Swedish Institute of Computer Science, Stanford University, the Mas-sachusetts Institute of Technology (MIT), Carnegie Mellon University (CMU), Cal Tech, Science University of Tokyo, Melbourne University, Calgary University, University of Wisconsin, Case Western Reserve, University of Oregon, Korea Advanced Institute of Science and Technology (KAIST), Quintus, Symbolics, IBM, and Xerox PARC. No proceedings were generated; instead, participants distributed copies of drafts, slides, and recent papers.