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Logic, Knowledge Representation, and Bayesian Decision Theory

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Computational Logic — CL 2000 (CL 2000)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1861))

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Abstract

In this paper I give a brief overview of recent work on uncertainty in AI, and relate it to logical representations. Bayesian decision theory and logic are both normative frameworks for reasoning that emphasize different aspects of intelligent reasoning. Belief networks (Bayesian networks) are representations of independence that form the basis for understanding much of the recent work on reasoning under uncertainty, evidential and causal reasoning, decision analysis, dynamical systems, optimal control, reinforcement learning and Bayesian learning. The independent choice logic provides a bridge between logical representations and belief networks that lets us understand these other representations and their relationship to logic and shows how they can extended to first-order rule-based representations. This paper discusses what the representations of uncertainty can bring to the computational logic community and what the computational logic community can bring to those studying reasoning under uncertainty.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of British Columbia, 2366Main Mall, Vancouver, B.C., Canada, V6T 1Z4

    David Poole

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  1. David Poole
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Editor information

Editors and Affiliations

  1. The Australian National University, Australia

    John Lloyd

  2. Simon Fraser University, Canada

    Veronica Dahl

  3. Universität Koblenz, Germany

    Ulrich Furbach

  4. The University of Birmingham, UK

    Manfred Kerber

  5. University of Manchester, UK

    Kung-Kiu Lau

  6. The Pennsylvania State University, USA

    Catuscia Palamidessi

  7. Universidade Nova de Lisboa, Portugal

    Luís Moniz Pereira

  8. The Hebrew University of Jerusalem, Israel

    Yehoshua Sagiv

  9. The University of Melbourne, Australia

    Peter J. Stuckey

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Poole, D. (2000). Logic, Knowledge Representation, and Bayesian Decision Theory. In: Lloyd, J., et al. Computational Logic — CL 2000. CL 2000. Lecture Notes in Computer Science(), vol 1861. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44957-4_5

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  • DOI: https://doi.org/10.1007/3-540-44957-4_5

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