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.

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."

Review of Expert Systems: Techniques, Tools, and Applications

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. 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.

The second Natural Language Understanding and Logic Programming (NLULP) Workshop was held on 17-19 August 1987 at Simon Fraser University, Vancouver, British Columbia, Canada, (the first NLULP workshop was held three years ago in Rennes, France).

I compare the big problems studied in artificial intelligence and related fields in order to understand some major changes -- both internal and external -- recently suffered by AI. The comparison finds AI with few problems to call its own, and I identify some further major changes that may occur soon.

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.