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.

Overview of GLISP GLISP is a LISP-based language which provides high-level language features not found in ordinary LISP. The GLISP language is implemented by means of a compiler which accepts GLISP as input and produces ordinary LISP as output; this output can be further compiled to machine code by the LISP compiler. The goal of GLISP is to allow structured objects to be reft-trenced in a convenient, succinct language, and to allow the structures of objects to be changed without changing the code which references the objects. The syntax of many GLISP constructs is English-like; much of the power and brevity of GLISP derive from the compiler features necessary to support the relatively informal.

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

The two AGE documents, Joy of AGE-ing and AGE Reference 111anual, do not give the readers a sense of how to go about formulating a program to be built using AGE. This document is intended to fill that gap by presenting a complete program--its formulation, its construction and code, and its runs. It is a very simple program and uses only a small subset of the features in AGE. However, many problems can be formulated in similar ways, and it can be used as a template for the beginning AGE users. For most effective use of this document, it should be read in conjunction with the other two AGE documents--specific references are made when appropriate.

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

Reserved Function Names 109 Appendix Ili.

Programs Based on the Blackboard Model 8 2.2.1.