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Modeling Uncertainty with Propositional Assumption-Based Systems

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Applications of Uncertainty Formalisms

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1455))

Abstract

This paper proposes assumption-based systems as an efficient and convenient way to encode uncertain information. Assumption based systems are obtained from propositional logic by including a special type of propositional symbol called assumption. Assumptions are needed to express the uncertainty of the given information. Assumption-based systems can be used to judge hypotheses qualitatively or quantitatively. This paper shows how assumption-based systems are obtained from causal networks, it describes how symbolic arguments for hypotheses can be computed efficiently, and it presents ABEL, a modeling language for assumption-based systems and an interactive tool for probabilistic assumption-based reasoning.

Research supported by grant No.2100-042927.95 of the Swiss National Foundation for Research.

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Author information

Authors and Affiliations

  1. Institute of Informatics, University of Fribourg, CH-1700, Fribourg, Switzerland

    Rolf Haenni

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  1. Rolf Haenni
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK

    Anthony Hunter

  2. Queen Mary and Westfield College, Department of Electronic Engineering, University of London, London, E1 4NS, UK

    Simon Parsons

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© 1998 Springer-Verlag Berlin Heidelberg

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Haenni, R. (1998). Modeling Uncertainty with Propositional Assumption-Based Systems. In: Hunter, A., Parsons, S. (eds) Applications of Uncertainty Formalisms. Lecture Notes in Computer Science(), vol 1455. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-49426-X_21

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  • DOI: https://doi.org/10.1007/3-540-49426-X_21

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65312-7

  • Online ISBN: 978-3-540-49426-3

  • eBook Packages: Springer Book Archive

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