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

"Computing can change our ways of thinking about many things, mathematics, biology, engineering, administrative procedures, and many more. But my main concern is that it can change our thinking about ourselves: giving us new models, metaphors, and other thinking tools to aid our efforts to fathom the mysteries of the human mind and heart. The new discipline of Artificial Intelligence is the branch of computing most directly concerned with this revolution. By giving us new, deeper, insights into some of our inner processes, it changes our thinking about ourselves. It therefore changes some of our inner processes, and so changes what we are, like all social, technological and intellectual revolutions." This book, published in 1978 by Harvester Press and Humanities Press, has been out of print for many years, and is now online, produced from a scanned in copy of the original, digitised by OCR software and made available in September 2001. Since then a number of notes and corrections have been added. Atlantic Highlands, NJ: Humanities Press.

For example, lnterlisp permits accessing the control stack at an unusually detailed level.

"The terms adaptation, learning, concept-formation, induction, self-organization, and self-repair have all been used in the context of learning system (LS) research. The research has been conducted within many different scientific communities, however, and these terms have come to have a variety of meanings. It is therefore often difficult to recognize that problems which are described differently may in fact be identical. Learning system models as well are often tuned to the require- ments of a particular discipline and are not suitable for application in related disciplines."In Encyclopedia of Computer Science and Technology, Vol. 11. Dekker

"A CASNET model consists of three main components: observations of a patient, pathophysiological states, and disease classifications. As observations are recorded, they are associated with the appropriate intennediate states. These states, in turn, are typically causally related, thereby forming a network that summarizes the mechanisms of disease. It is these patterns of states in the network that are linked to individual disease classes." Artificial intelligence, August, 1978. Reprinted in Clancey & Shortliffe. Readings in Medical Artificial Intelligence: The First Decade. Ch. 7.

Master skill--operational in the sense-t'hat it can be run on Another form of the'Master skill' aspiration aims at correct'strong mastery' in this sense is attainable for the complete None of the above listed endgames contains anything problematical from a Master's point of view and computer programs Using a vocabulary which is defined in Kmoch's (1959) 'An enemy pawn ahead on the same file is a counterpawn, Some of these relations may be very useful if developed further. For expmple, if a pawn is'overloaded', in that it is pefforming Defence Diagram, see Figure 1). A rule is applied'to a position (in a manner familiar to'forcing tree' that guarantees the achievement of better-goals The'and-or' tree search, carried out by module 1 of the AU Figure 1 The ADD corresponding to the position shown in Figure 1. The Computer Journal ' HOW DIFFICULT IS THE KNKR PROBLEM? Longest variation in Fine before capture of the Knight: 24 moves; longest known variation 27 moves.

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

Artificial Intelligence and Pattern Recognition in Computer Aided Design. New York: North Holland: 250-257

An analysis of the efficiency of the alpha-beta algorithm is carried out based on a probabilistic model in which terminal node scores depend on random branch values. Explicit expressions are derived for the expected number of terminal nodes scored for the cases of uniform trees of fanout N and of depths 2 and 3. For trees of depth 2, the expected number is of order O(NHN); for trees of depth 3, the expected number is of order O(N2). An upper bound on the expected number of terminal nodes scored for trees of depth 4 is shown to be no greater than O(N2HN2) and no less than O(N2).

Chess programs can differ in depth of search or in the evaluation function applied to leaf nodes or both. Over the past 10 years, the notion that the principal way to strengthen a chess program is to improve its depth of search has held sway. Improving depth of search undoubtedly does improve a program's strength. However, projections of potential gain have time and again been found to overestimate the actual gain. We examine the notion that it is possible to project the playing strength of chess programs by having different versions of the same program (differing only in depth of search) play each other.