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The axioms for information algebras are derived from the axiom system proposed in Shenoy and Shafer, , see also Shafer, From Wikipedia, the free encyclopedia.

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March Categories : Information theory Abstract algebra. Hidden categories: Articles needing cleanup from August All pages needing cleanup Cleanup tagged articles with a reason field from August Wikipedia pages needing cleanup from August Articles to be expanded from March All articles to be expanded Articles using small message boxes CS1 errors: deprecated parameters CS1: long volume value CS1 errors: chapter ignored. Namespaces Article Talk. Views Read Edit View history. Generic Inference : a Unifying Theory for Automated Reasoning by Marc Pouly 8 editions published between and in English and held by WorldCat member libraries worldwide This book provides a rigorous algebraic study of the most popular inference formalisms with a special focus on their wide application area, showing that all these tasks can be performed by a single generic inference algorithm.

Generic Inference: A Unifying Theory for Automated Reasoning

Written by the leading international authority on the topic, it includes an algebraic perspective study of the valuation algebra framework , an algorithmic perspective study of the generic inference schemes and a "practical" perspective formalisms and applications. Researchers in a number of fields including artificial intelligence, operational research, databases and other areas of computer science; graduate students; and professional programmers of inference methods will benefit from this work.

In recent years, it has become apparent that an important part of the theory of AI is concerned with reasoning on the basis of uncertain, vague, incomplete, or inconsistent information. A variety of nonclassical formalisms, both symbolic and numerical, have been developed and are addressed in this volume; among them are nonmonotonic and modal logics, fuzzy sets, possibility theory, believe functions, evidence theory, dynamic models, and Bayesian networks.

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It develops the Dempster-Shafer Theory as a theory of the reliability of reasoning with uncertain arguments. A particular interest of this approach is that it yields a new synthesis and integration of logic and probability theory. The reader will benefit from a new view at uncertainty modeling which extends classical probability theory.

It refers to different, but related questions.

Generic Inference

Therefore information needs to be aggregated and focused onto the relevant questions. Considering combination and focusing of information as the relevant operations leads to a generic algebraic structure for information. This book introduces and studies information from this algebraic point of view.

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Algebras of information provide the necessary abstract framework for generic inference procedures. They allow the application of these procedures to a large variety of different formalisms for representing information.


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At the same time they permit a generic study of conditional independence, a property considered as fundamental for knowledge presentation. Information algebras provide a natural framework to define and study uncertain information.

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Uncertain information is represented by random variables that naturally form information algebras. This theory also relates to probabilistic assumption-based reasoning in information systems and is the basis for the belief functions in the Dempster-Shafer theory of evidence.

Simulationstechnik : Entwurf und Simulation von Systemen auf digitalen Rechenautomaten by Kurt Bauknecht Book 12 editions published between and in German and held by WorldCat member libraries worldwide.

Inferring and Executing Programs for Visual Reasoning

The various theoretical and modelling aspects of defeasible reasoning were dealt with in the first four volumes, and this volume now turns to the algorithmic aspect. Topics covered include: Computation in valuation algebras; consequence finding algorithms; possibilistic logic; probabilistic argumentation systems, networks and satisfiability; algorithms for imprecise probabilities, for Dempster-Shafer, and network based decisions.