This series will include monographs and collections of studies devoted to the investigation and exploration of knowledge, information, and data processing systems of all kinds, no matter whether human, (other) ani mal, or machine. Its scope is intended to span the full range of interests from classical problems in the philosophy of mind and philosophical psy chology through issues in cognitive psychology and sociobiology (concerning the mental capabilities of other species) to ideas related to artificial intelli gence and computer science. While primary emphasis will be placed upon theoretical, conceptual, and epistemological aspects of these problems and domains, empirical, experimental, and methodological studies will also ap pear from time to time. The present volume provides a collection of studies that focus on some of the central problems within the domain of artificial intelligence. These difficulties fall into four principal areas: defeasible reasoning (including the frame problem as apart), ordinary language (and the representation prob lems that it generates), the revision of beliefs (and its rules of inference), and knowledge representation (and the logical problems that are encountered there). These papers make original contributions to each of these areas of inquiry and should be of special interest to those who understand the crucial role that is played by questions of logical form. They vividly illustrate the benefits that can emerge from collaborative efforts involving scholars from linguistics, philosophy, computer science, and AI. J. H. F."

Coping with uncertainty is a necessary part of ordinary life and is crucial to an understanding of how the mind works. It is a vital element in developing artificial intelligence that will not be undermined by its own rigidities. There have been many approaches to the problem of uncertain inference, ranging from probability to inductive logic to nonmonotonic logic. This book seeks to provide a clear exposition of these approaches within a unified framework.

Coping with uncertainty is a necessary part of ordinary life and is crucial to an understanding of how the mind works. It is a vital element in developing artificial intelligence that will not be undermined by its own rigidities. There have been many approaches to the problem of uncertain inference, ranging from probability to inductive logic to nonmonotonic logic. This book seeks to provide a clear exposition of these approaches within a unified framework.

Everyone knows it is easy to lie with statistics. It is important then to be able to tell a statistical lie from a valid statistical inference. It is a relatively widely accepted commonplace that our scientific knowledge is not certain and incorrigible, but merely probable, subject to refinement, modifi cation, and even overthrow. The rankest beginner at a gambling table understands that his decisions must be based on mathematical ex pectations - that is, on utilities weighted by probabilities. It is widely held that the same principles apply almost all the time in the game of life. If we turn to philosophers, or to mathematical statisticians, or to probability theorists for criteria of validity in statistical inference, for the general principles that distinguish well grounded from ill grounded generalizations and laws, or for the interpretation of that probability we must, like the gambler, take as our guide in life, we find disagreement, confusion, and frustration. We might be prepared to find disagreements on a philosophical and theoretical level (although we do not find them in the case of deductive logic) but we do not expect, and we may be surprised to find, that these theoretical disagreements lead to differences in the conclusions that are regarded as 'acceptable' in the practice of science and public affairs, and in the conduct of business."

Measurement is fundamental to all the sciences, the behavioural and social as well as the physical and in the latter its results provide our paradigms of 'objective fact'. But the basis and justification of measurement is not well understood and is often simply taken for granted. Henry Kyburg Jr proposes here an original, carefully worked out theory of the foundations of measurement, to show how quantities can be defined, why certain mathematical structures are appropriate to them and what meaning attaches to the results generated. Crucial to his approach is the notion of error - it can not be eliminated entirely from its introduction and control, her argues, arises the very possibility of measurement. Professor Kyburg's approach emphasises the empirical process of making measurements. In developing it he discusses vital questions concerning the general connection between a scientific theory and the results which support it (or fail to).