I will remain Ion8 and deeply indebted to Herb Simon, H. T. Kung, AI Newell, Jay

How do practicing physicians make clinical decisions? What techniques can we use in the computer to produce programs that exhibit medical expertise?

—A class of LISP programs that is analogous to the finite-state automata is defined, and an algorithm is given for constructing such programs from examples of their input-output behavior. It is shown that the algorithm has robust performance for a wide variety of inputs and that it converges to a solution on the basis of minimum input information.IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS, VOL. SMC-8, NO. 8,

" The author wishes to thank Eileen Fitzpatrick for her helpful suggestions.

Retrospective on lessons learned from the Dendral project."The DENDRAL and Meta-DENDRAL programs are products of a large, interdisciplinary group of Stanford University scientists concerned with many and highly varied aspects of the mechanization of scientific reasoning and the formalization of scientific knowledge for this purpose. An early motivation for our wok was to explore the power of existing Al methods, such as heuristic search, for reasoning in difficult scientific problems. Another concern has been to exploit the AI methodology to understand better some fundamental questions in the philosophy of science, for example the processes by which explanatory hypotheses are discovered or judged adequate. From the start, the project has had an applications dimension. It has sought to develop "expert level" agents to assist in the solution of problems in their discipline that require complex symbolic reasoning. The applications dimension is the focus of this paper."Artificial Intelligence 11 (1-2): 5-24

Such features include spelling correction, processing of incomplete (ell ipt ic-I) input?, jntfrrog-t ior of th

Hierarchies are observed in the levels of description used in image understanding along a few dimensions: processing unit, detail, composition and scene/view distinction. Emphasis is placed on the importance of explicitly handling the hierarchies both in representing knowledge and in using it. A scheme of "knowledge block" representation which is structured along the processing-unit hierarchy is also presented. I. INTRODUCTION Image Understanding System(IUS) constructs a description of the scene being viewed from an array of image sensory data: intensity, color, and sometimes range data. Image understanding is best characterized by description, whereas pattern recognition by classification, and image processing by image output.

I shall refer to such restrictions as preference restrictions, because of the way the present NLUS is already able to accept natural language that violates preferences, as (1) does (see recap in next section for more detail). Such usage as (s) will be referred to as extended, or preference violating, and these will serve instead of the more literary and philosophical term "metaphorical". It is an important assumption of this paper that such usage is the norm in ordinary everyday language use, and cannot be relegated to the realm of the exceptional, or the odd, and so dealt with by considerations of "performance". On the contrary it is, I would argue, central to our language capabilities, and any theory of language must have something concrete to say about it. Even if the newspaper usages above are "extended", I would suggest that anyone who could not grasp these extension could not be said to understand English properly (given adequate knowledge from which to extend, and we shall come to that.) It will be obvious already that the commitment to a norm implies a corresponding commitment to general everyday language as a proper topic for Al.

In English, "1ikewise", "too", "similarly", etc., followed optionally by a preposition and then by a noun phrase indicate that the role filled by the new noun phrase in a previously mentioned predicate is a non-subject one.

Traditional scheduling algorithms (using the techniques of PERT charts, decision analysis or operations rrsrarrh) require well-defined, quantitative, complete sets of constrainls*. They are insufficient for scheduling situations where the problem description is ill-defined, involving incomplete, possibly inconsistent and generally qualitative constraints. The NUDGE program uses an extensive knowledge base to debug scheduling requests by supplying typical values for qualitative constraints, supplying missing details and resolving minor inconsistencies. The result is that an informal request is converted to a complete description suitable for a traditional scheduler. To implement the NUDGE program, a knowledge representation language -- FRL-0 -- based on a few powerful generalizations of the traditional property list representation has been developed.