Alan Ross Anderson

J. Michael Dunn

Nuel D. Belnap

Entailment

Nuel D. Belnap Jr.

In spite of a powerful tradition, more than two thousand years old, that in a valid argument the premises must be relevant to the conclusion, twentieth-century logicians neglected the concept of relevance until the publication of Volume I of this monumental work. Since that time relevance logic has achieved an important place in the field of philosophy: Volume II of Entailment brings to a conclusion a powerful and authoritative presentation of the subject by most of the top people working in the area. Originally the aim of Volume II was simply to cover certain topics not treated in the first volume--quantification, for example--or to extend the coverage of certain topics, such as semantics. However, because of the technical progress that has occurred since the publication of the first volume, Volume II now includes other material. The book contains the work of Alasdair Urquhart, who has shown that the principal sentential systems of relevance logic are undecidable, and of Kit Fine, who has demonstrated that, although the first-order systems are incomplete with respect to the conjectured constant domain semantics, they are still complete with respect to a semantics based on "arbitrary objects." Also presented is important work by the other contributing authors, who are Daniel Cohen, Steven Giambrone, Dorothy L. Grover, Anil Gupta, Glen Helman, Errol P. Martin, Michael A. McRobbie, and Stuart Shapiro. Robert G. Wolf's bibliography of 3000 items is a valuable addition to the volume. Originally published in 1992. The Princeton Legacy Library uses the latest print-on-demand technology to again make available previously out-of-print books from the distinguished backlist of Princeton University Press. These editions preserve the original texts of these important books while presenting them in durable paperback and hardcover editions. The goal of the Princeton Legacy Library is to vastly increase access to the rich scholarly heritage found in the thousands of books published by Princeton University Press since its founding in 1905.

The Logic of Relevance and Necessity

Entailment, Vol. II

Min Chen

Amos Golan

Aman Ullah

"Info-metrics is a framework for rational inference on the basis of limited, or insufficient, information. It is the science of modeling, reasoning, and drawing inferences under conditions of noisy and insufficient information. Info-metrics has its roots in information theory (Shannon, 1948), Bernoulli's and Laplace's principle of insufficient reason (Bernoulli, 1713) and its offspring the principle of maximum entropy (Jaynes, 1957). It is an interdisciplinary framework situated at the intersection of information theory, statistical inference, and decision-making under uncertainty. Within a constrained optimization setup, info-metrics provides a simple way for modeling and understanding all types of systems and problems. It is a framework for processing the available information with minimal reliance on assumptions and information that cannot be validated. Quite often a model cannot be validated with finite data. Examples include biological, social and behavioral models, as well as models of cognition and knowledge. The info-metrics framework extends naturally for tackling these types of common problems"--

Information and Information Processing Across Disciplines

Advances in Info-Metrics

J. Michael Dunn

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