Carnegie-Mellon University's Hitech chess computer scored 5-1 in the National Open Chess Championships held in Chicago March 18-20. The Championship Section in which Hitech competed, had 380 entries. Carnegie-Mellon University's Hitech chess computer scored 5-1 in the National Open Chess Championships held in Chicago March 18-20. The Championship Section in which Hitech competed, had 380 entries.

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). 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 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.

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 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.

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. 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. 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.

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. 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 guest editorial describing the special issue on spatial reasoning: "We conceive of space as a completely empty, infinite, three-dimensional, isotropic, disembodied receptacle distinct from the earth or any object that might be located on the earth, one that is capable of housing not only things but also such incorporeal mathematical entities as points and infinite straight lines. Such a strange idea -especially if it were taken to describe something that exists in this world-was unthinkable before the seventeenth century; yet not even Galileo fully accepted the idea of such a world as real. For him, a "straight line" was still bound to the earth's surface. The transformation that led to the reification of geometry, though basically one of attitude and perception rather than of empirical observation, profoundly affected the course of science."

This is Part 2 of a two-part article and discusses issues of tort liability and the use of computers in the courtroom. Part 1 of this article, which appeared in the Spring 1988 issue of AI Magazine, discussed steps that developers of AI systems can take to protect their efforts, and the attendant legal ambiguities that must eventually be addressed in order to clarify the scope of such protection. Part 2 explores the prospect of AI systems as subjects of litigation.

Sometimes less ambiguous, are extremely complex.