"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.

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

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

In A. Hanson and E. Riseman (Eds.), Computer vision systems. New York: Academic Press, 335-352.

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

In C. W. Savage (Ed.), Perception and cognition: Issues in the foundations of psychology. Minneapolis: University of Minnesota Press.

Semantic networks constitute one of the many attempts to capture human knowledge in an abstraction suitable for processing by computer program. While semantic nets enjoy widespread popularity, they seem never to live up to their authors' expectations of expressive power and ease of construction. This paper examines the fundamentals of network notation, in order to understand why the “formalism” has not been the panacea it was once hoped to be. We focus here on “concepts”—what net-authors think they are, and how network nodes might represent them. The simplistic view of concept nodes as representing extensional sets is examined, and found wanting in several respects.

A wide range of segmentation techniques continues to evolve in the literature on scene analysis. Many of these approaches have been constrained to limited applications or goals. This survey analyzes the complexities encountered in applying these techniques to color images of natural scenes involving complex textured objects. It also explores new ways of using the techniques to overcome some of the problems which are described. An outline of considerations in the development of a general image segmentation system which can provide input to a semantic interpretation process is distributed throughout the paper.

This paper describes a problem-solving framework in which aspects of mathematical decision theory are incorporated into symbolic problem-solving techniques currently predominant in artificial intelligence. The utility function of decision theory is used to reveal tradeoffs among competing strategies for achieving various goals, taking into account such factors as reliability, the complexity of steps in the strategy, and the value of the goal. The utility function on strategies can therefore be used as a guide when searching for good strategies. It is also used to formulate solutions to the problems of how to acquire a world model, how much planning effort is worthwhile, and whether verification tests should be performed. These techniques are illustrated by application to the classic monkey and bananas problem.