MRS is a knowledge representation systmt intended for use by Al researchers in building expert systems. It offers a diverse repertory of commands for asserting and retrieving information, with various representatiuns (e.g. The initial system includes a vocabulary of concepts and facts about logic, sets, mappings, arithmetic, and procedures. What differentiates MRS from many other knowledge representation systems is its ability to observe, reason about, and control its own activity. In MRS the system is treated as a domain in its own right.

Artificial intelligence, long a topic of basic computer science research, is now being applied to problems of scientific, technical, and commercial interest. Some consultation programs, though limited in versatility, have achieved levels of performance rivaling those of human experts. A collateral benefit of this work is the systematization of previously unformalized knowledge in areas such as medical diagnosis and geo!ogy.

List of Tables Table 4-1: Status determined based on the ''alues returned by selection rules 13 ACKNOWLEDGMENTS 1 would like to thank

The application of Artificial Intelligence techniques to real-world problems has produced promising research results, but seldom has a system becoma a useful tool in its domain of expertise.

I cis is the final report for Contract N-00014-79-C-03C2, covering the period of 15 March 1979 through 14 March 1982. The goal of the project was to develop methods for representing teaching and problem-solving knowledge in computer-based tutorial systems. One focus of the work was formulation of principles for managing a case method tutorial dialogue; the other major focus was investigation of the use of a production rule representation for the subject material of tutorial program. The main theme pursued by this research is that representing teaching and problemsolving knowledge separately and explicitly enhances the ability to build, modify and test complex tutorial programs. Two major corr Jter programs were constructed.

No. STAN-CS-82-909 Also numbered: HPP42-7 Department of Computer Science Stanford University Stanford, CA 94305 Abstract This paper describes the student modeler of the GUIDON2 tutor, which understands plan: by a dual search strategy. It first produces multiple predictions of student behavior by a model-driven simulation of the expert. Focused, data-driven searches then explain incongruities. By supplementing each other, these methods lead to an efficient and robust plan understander for a complex domain. Diagnostic problem-solving requires domain knowledge and a plan for applying that knowledge to the problem.

Room echoes in which the signal analysis was taking place.

The most widely used description level in integrated circuit design is the artwork or layout level. This level describes integrated circuits in terms of "colored rectangles" (representing material on a chip) that can be composed to build up large designs. Associated with the colored rectangle terms of the layout level is a set of composition rules, called layout design rules. The layout composition rules provide a simple shallow model of composition that is based on a deep model of electrical properties and fabrication tolerances. If designers follow these rules, their designs are guaranteed to have adequate physical spacing on a chip [3, 4].

OMEN is an object-oriented programming system designed for use in a FRANI LISP or other similar programming environment. OMEN stands for OHiccr MANIPULATION ENVIRONNIFN r, and consists of a set of functions to be loaded on top of a!ASP system running MRS. The user can the use the functions provided by OMEN to create classes of objects, instances of those classes, and functions that operate on those objects, and to send messages to those objects. OMEN is similar in design and operation to the flavors system of Lisp Machine lisp and the LOOPS system for the Xerox Dolphin. OMEN was originally designed as a programming eny ironmcnt for an objectoriemed graphics system, but the system should be useful for many different applications. OMEN is not a programming language. It is a way of abstracting the data structures a program must use and the functions that operate on those data structures.

During the quarter century since the birth of the branch of computer science known as artificial intelligence (Al), much of the research has focused on developing symbolic models of human inference. In the last decade several related Al research themes have come together to form what is now known as "expert systems research."1 In this paper we review Al and expert systems to acquaint the reader with the field and to suggest ways in which this research will eventually be applied to advanced medical monitoring.