Reserved Function Names 109 Appendix Ili.

Programs Based on the Blackboard Model 8 2.2.1.

"Learning", the incorporation of additional knowledge into expert systems, ranges from human data entry (learning by being told), to data gathering (learning by observing), to full-fledged theory formation (learning by discovery). One important kind of learning is the compiling of descriptive meta-knowledge into strategic form, recasting it into a form in which it can be evaluated efficiently. Much of what we earlier called strategic meta-knowledge may be seen to be operationalized "caches" of descriptive or systemic meta-knowledge. For instance, R9 and R10 can be converted from systemic to strategic form by slight rewordings of their actions.

This papei describes a device-independent diagnostic program called DART. DART differs from previous approaches to diagnosis taken in the Artificial Intelligence community in that it works directly from design descriptions rather than MYCIN-like symptom-fault rules. DART differs from previous approaches to diagnosis taken in the design automation community in that is more general and in many cases more efficient. DART uses a device-independent language for describing devices and a device-independent inference procedure for diagnosis. The resulting generality allows it to be applied to a wide class of devices ranging from digital logic to nuclear reactors. Although this generality engenders some computational overhead on small problems, it facilitates the use of multiple design descriptions and thereby makes possible combinatoric savings that more than offsets this overhead on problems of realistic size.

Search 4. Methods for assigning credit and blame--obtaining a local Performance Standard

This manual describes a domain-independent system, called EMYCIN, for constructing one class of expert computer programs: rule-based consultants. The resulting programs use knowledge specific to a problem domain to provide consultative advice to a client. The system-building tool, EMYCIN, is based on the domain-independent core of the MYCIN program. Domain kno,./ledge is represented in EMYCIN systems primarily as production rules, which are applied by a goal-directed backwardchaining control structure. Rules and consultation data may have associated measures of certainty, and incomplete data entry is allowed. The system includes an explanation facility that can display the line of reasoning followed by the consultation program, and answer questions from the client about the contents of its knowledge base. To aid the system designer in producing a knowledge base for a domain quickly and accurately, EMYCIN provides the following features: (1) a terse.

They do not necessarily reflect those of the Xerox Corporation, Stanford University, or the University of Southern California. This study was funded in part through the SUMEX Computer Project at Stanford University under grant 1212-007R5 from the Biotechnology Resources Program of the National Institutes of Health. I. INTRODUCTION Since November 1979, a group at the Information Sciences Institute of the University of Southern California has been working on ell implementation of Interlisp for the DEC VAX-scries1 computers. 'Ibis report is a description of the current status, future prospects, and estimated character of that Interlisp-VAX implementation. It is the result of several days of discussion with those at ISI involved with the implementation (Dave Dyer, Hans Koomen, Ray Bates. Dan Lynch); with John L. White of MIT, who is working on an implementation of another Lisp for the VAX (NIL); with the implementors of Interlisp-Jericho at 1311N (Alice Hartley, Norton Grecnfeld, Martin Yonkc, John Vittal, Frank Zdybel, Jeff Gibbons, Wryle Lewis); with the implementors of Franz Lisp and Berkeley Unix2 at U.C. Berkeley (Richard Fateman, Bill Joy, Keith Sklower, John Foderaro); and with my colleagues at Xerox PARC. An earlier draft of this report was circulated to the parties involved in the Interlisp-VAX discussions. 'Ibis document has been revised as a result of comments received.

This paper presents an "expert system" devised to aid organic chemists in determining the structure (i.e. the arrangement of atoms and bonds) of newly isolated, naturally occurring compounds. The system exploits a data base of rules for analyzing.013 C nuclear magnetic resonance spectra" [2. 13 C spectroscopy is a relatively new technique; only in the last ten years, at most, has this analytic approach been practical. Currently, no general interpretive schemes exist for thoroughly analyzing 13C spectra. A few limited classes of compounds have been investigated in detail, and highly specific schemes for interpreting their 13C spectra have been developed. The only generally applicabl- "interpretation rules" rely on correlation J of spectral and substructural features.

This paper is the author's contribution to Chapter 6. in the volume BU:LDIN3 EXPERT SYSTEMS, edited by R. Hayes-Roth, D. Lenat, and D. Waterman:4 The full article is entitled "Evaluation of expert systems: issues and case studies", and is authored by J. Gaschnig, P. Klahr, H. Pople, E. Shortliffe. The volume is the result of a Workshop on Expert Systems held in San Diego in August 1980 and sponsored by the Rand Corporation, ARPA, and the NSF. Parts of Chapters 7 & 8. Reprinted with permission. In this section we define ma,ly of the parameters that determine an appropriate design for an evaluation experiment. When one examines the literature on computer performance evaluation, it is clear that the term is used with a variety of meanings depending upon the;.-c.3pective of tne authors. Each tends to focus on the specific performance issues that have been most central to the design of the system in question. Utner aspects warranting formal evaluation are often ignored.

Reprinted from IJCAI, August 24-28, 1981, Vancouver, British Columbia. Used by permission of the International Joint Conference on Artificial Intelligence, Inc.; copies of the Proceedings are available from Morgan Kaufmann Publishers, Inc., 95 First Street, Los Altos, CA 94022, USA. DART: An Expert System for Computer Fault Diagnosis James S. Bennett Heuristic Programming Project, Computer Science Deparunent Stanford University, Stanford, CA 94305 Clifford R. Hollander IBM Scientific Center, 1530 Page Mill Road Palo Alto, CA 94304 To appear in International Joint Conference on Artificial Intelligence, August 24-28, 1981, Vancouver, British Columbia. A. Intro Juction We describe an application of artificial intelligence techniques to computer system fault diagnosis, in particular, we have implemented an automated consultant that advises IBM field service personnel on the diagnosis of faults occurring in computer installations. The consultant identifies specific system components (both hardware and software) likely to be responsible for an observed fault and offers a brief explanation of the major factors and evidence supporting these indictments.