The first computers were numerical calculators that did not appear to embody much intelligence at all. But before these machines were even designed.

In this paper I take an empirical look at the question of whether there are rational memckis of discovery and claim that computer programs provida a laboratory for experimentation on this question Recent work in artificial intelligence or Al. has produced programs capaole of serious intellectual work in science Results from Al,viii be used to show that there exist mechanized procedures for discw.ering

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.

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.