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.

Sometimes less ambiguous, are extremely complex.

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.

The Computing Research Laboratory (CRL) at New Mexico State University is a center for research in artificial intelligence and cognitive science. Specific areas of research include the human-computer interface, natural language understanding, connectionism, knowledge representation and reasoning, computer vision, robotics, and graph theory. This article describes the ongoing projects at CRL.

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.

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

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.

VT (vertical transportation) is an expert system for handling the design of elevator systems that is currently in use at Westinghouse Elevator Company. Although VT tries to postpone each decision in creating a design until all information that constrains the decision is known, for many decisions this postponement is not possible. In these cases, VT uses the strategy of constructing a plausible approximation and successively refining it. VT uses domain-specific knowledge to guide its backtracking search for successful refinements. The VT architecture provides the basis for a knowledge representation that is used by SALT, an automated knowledge-acquisition tool. SALT was used to build VT and provides an analysis of VT's knowledge base to assess its potential for convergence on a solution.