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].

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.

This paper* proposes the use of explicit austraction levels to organize decision making in digital design. These levels partition the concerns that a designer must consider at any time. They provide terms and composition rules for the composition of structural descriptions within a level. This allows multiple opportunities for mapping behavior into structure. A version of this paper was presented at the Conference on Advanced Research in VLSI, Massachusetts Institute of Technology, Cambridge, Massachusetts, January 25-27, 1982.

ONCOCIN's task is to assist oncologists with of computer-based decision aids by physicians. It the management of cancer patients undergoing was clear that diagnostic accuracy was not enough structured (protocol) treatment for their disease.

'ince much of cancer chemotherapy research this reasoning system with a high speed interface today is dependent upon rigorously designed and to help make the system acceptable to oncologists.

During the quarter century since the birth of "artificial intelligence" (Al), attempts to develop symbolic models of human reasoning processes have been a major focus of the ongoing research. It is only in the last half-dozen years or so, however, that several related Al research themes have come together in the formation of what is now known as "expert systems researoh" CI], In this brief paper I would 1.ke to review the key aspects of A: and expert syste-.s

Each example in this series illustrates a different set of features of AGE. AGE Example Series: Number 1 describes a beginner's program.