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

It is essentially a Horn clause theorem prover augmented with a special inference rule for'dealing with

Tech. Rep. 118, Dept. of Computer Science, University of Toronto.

"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

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.

Montague's dUficu/t nulalian and complex model theory have tended to obscure potential insights for

"An important research problem in artificial intelligence is the study of methods for learning general concepts or rules from a set of training instances. An approach to this problem is presented which is guaranteed to find, without backtracing, all rule versions consistent with a set of positive and negative training instances. The algorithm put forth uses a representation of the space of those rules consistent with the observed training data. This "rule version space" is modified in response to new training instances by eliminating candidate rule versions found to conflict with each new instance. The use of version spaces is discussed in the context of Meta-DENDRAL, a program which learns rules in the domain of chemical spectroscopy."Proc. IJCAI 77 VOL 1 MASSACHUSETTS INSTITUTE OF TECHNOLOGY CAMBRIDGE, MASSACHUSETTS, USA AUGUST 22 - 25 , 1977, pp.305-310

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