These paradigms include 1) clinical algorithms, 2) statistical analysis of collections of patient data, 3) mathematical models of physical processes, 4) decision analysis.

This article reviews the strengths and limitations of five major paradigms of medical computer-assisted decision making (CAOM): 1) clinical algorithms.

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.

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.

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.

This paper briefly outlines the difference between Branching on the bas of response was the first step coward traditional instructional programs and MAI.

Theorem Proving Vision Robotics Information Processing Psychology Learning and Inductive Inference Planning and Related Problem-solving Techniques A. Overview There are two main area.

It is noted that no one method is best with a view to the potential role of automated decision for all applications. However, emphasis is given to the limitations of early work that have made artificial intelligence techniques and knowledge aids in that domain 1611.

A clinical investigator graphical capabilities which can plot specific parameters for a keeping the records of his study patients on such a system can patient over time 1126]. However, it is in the analysis of stored use the program's statistical capabilities for data analysis.

Paper presented at the IPN Symposium 7: Formalized Theories of Thinking and Learning and Their Implications for Science Instruction.