Artificial intelligence, or AI, is largely an experimental science—at least as much progress has been made by building and analyzing programs as by examining theoretical questions. MYCIN is one of several well-known programs that embody some intelligence and provide data on the extent to which intelligent behavior can be programmed. As with other AI programs, its development was slow and not always in a forward direction. But we feel we learned some useful lessons in the course of nearly a decade of work on MYCIN and related programs. In this book we share the results of many experiments performed in that time, and we try to paint a coherent picture of the work. The book is intended to be a critical analysis of several pieces of related research, performed by a large number of scientists. We believe that the whole field of AI will benefit from such attempts to take a detailed retrospective look at experiments, for in this way the scientific foundations of the field will gradually be defined. It is for all these reasons that we have prepared this analysis of the MYCIN experiments.

The complete book in a single file.

Various groups of ascertainable individuals have been granted the status of "persons" under American law, while that status has been denied to other groups. This article examines various analogies that might be drawn by courts in deciding whether to extend "person" status to intelligent machines, and the limitations that might be placed upon such recognition. As an alternative analysis, this article questions the legal status of various human/machine interfaces, and notes the difficulty in establishing an absolute point beyond which legal recognition will not extend.

This article describes a theory of reasoning about uncertainly, based on a representation of states of certainly called endorsements. The theory of endorsements is an alternative to numerical methods for reasoning about uncertainly, such as subjective Bayesian methods (Shortliffe and Buchanan, 1975; Duda hart, and Nilsson, 1976) and Shafer-dempster theory (Shafer, 1976). The fundamental concern with numerical representations of certainty is that they hide the reasoning about uncertainty. While numbers are easy to propagate over inferences, what the numbers mean is unclear. The theory of endorsements provide a richer representation of the factors that affect certainty and supports multiple strategies for dealing with uncertainty.

Various groups of ascertainable individuals have been granted the status of "persons" under American law, while that status has been denied to other groups. This article examines various analogies that might be drawn by courts in deciding whether to extend "person" status to intelligent machines, and the limitations that might be placed upon such recognition. As an alternative analysis, this article questions the legal status of various human/machine interfaces, and notes the difficulty in establishing an absolute point beyond which legal recognition will not extend.

This book describes a theory of memory representation, organization, and processing for understanding complex narrative texts. The theory is implemented as a computer program called BORIS which reads and answers questions about divorce, legal disputes, personal favors, and the like. The system is unique in attempting to understand stories involving emotions and in being able to deduce adages and morals, in addition to answering fact and event based questions about the narratives it has read. BORIS also manages the interaction of many different knowledge sources such as goals, plans, scripts, physical objects, settings, interpersonal relationships, social roles, emotional reactions, and empathetic responses. The book makes several original technical contributions as well.

Research by members of the Department of Computer Science at Rutgers, and by their collaborators, is organized within the Laboratory for Computer Science research(LCSR). AI and AI-related applications are the major area of research within LCSR, with about forty people-faculty, staff and graduate students-currently involved in various aspects of AI research.

The Nobel Prize in Economics was awarded to Herbert Simon in 1978. At Carnegie-Mellon University he holds the title of Professor of Computer Science and Psychology. These two facts together delineate the range and uniqueness of his contributions in creating meaningful interactions among fields that developed in isolation but that are all concerned with human decision-making and problem-solving processes. In particular, Simon has brought the insights of decision theory, organization theory (especially as it applies to the business firm), behavior modeling, cognitive psychology, and the study of artificial intelligence to bear on economic questions. This has led not only to new conceptual dimensions for theoretical constructions, but also to a new humanizing realism in economics, a way of taking into account and dealing with human behavior and interactions that lie at the root of all economic activity.

The notion of an ethical machine can be interpreted in more than one way. Perhaps the most important interpretation is a machine that can generalize from existing literature to infer one or more consistent ethical systems and can work out their consequences. An ultra-intelligent machine should be able to do this, and that is one reason for not fearing it.In Hayes, J. E., Michie, D., and Pao, Y.-H. (Eds.), Machine Intelligence 10. Ellis Horwood.

Equation Time Methods Used (I) 2200 Function Swapplng,Polysolve (2) 1905 Function Swapping,Isolation (3) 6280 Homogenization,Function Swapping, (4) I010 (5) 1350 (6) 815 (7) 3580 The numbered equations refer to given in milliseconds. Polysolve,Isolation Homogenization,Polysolve,Isolation Homogenization,Polysolve,Isolation Attraction,Collection,Isolation The following table Homogenlzation,Polysolve,Isolation those given in the introduction. Times are CPU times REFERENCES [Borning and Bundy 81] Borning, A and Bundy, A. Using matching in algebraic equation solving.

Our purpose in studying natural language understanding in conjunction with problem solving is to bring together the constraints of what formal representation can actually be obtained with the question of what knowledge is required in order to solve a wide range of problems in a semantically rich domain. We believe that these issues cannot sensibly be tackled in isolation. In practical terms we have had the benefits of an increased awareness of common problems in both areas and a realisation that some of our techniques are applicable to both the control of inference and the control of parsing. Early work on solving mathematical problems stated in natural language was done by Bobrow (STUDENT - (i]) and Chamiak (CARPS - [5]). However the rudimentary parsing and simple semantic structures used by Bobrow and Charniak are inadequate for any but the easiest problems. Our intention has been to build on B/RG Chris This work was supported by SRC grant number 94493 and an SRC research studentship for Mellish.