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Probabilistic Argumentation

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Probabilistic Logics and Probabilistic Networks

Part of the book series: Synthese Library ((SYLI,volume 350))

Abstract

Degrees of support and possibility are the central formal concepts in the theory of probabilistic argumentation (Haenni, 2005a, 2009; Haenni et al., 2000; Kohlas, 2003). This theory is driven by the general idea of putting forward the pros and cons of a proposition or hypothesis in question. The weights of the resulting logical arguments and counter-arguments are measured by probabilities, which are then turned into (sub-additive1) degrees of support and (super-additive) degrees of possibility.

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References

  1. Dempster, A. P. (1968). A generalization of Bayesian inference. Journal of the Royal Statistical Society, 30:205–247.

    Google Scholar 

  2. Haenni, R. (2005b). Using probabilistic argumentation for key validation in public-key cryptography. International Journal of Approximate Reasoning, 38(3):355–376.

    Article  Google Scholar 

  3. Haenni, R. (2009). Probabilistic argumentation. Journal of Applied Logic, 7(2):155–176.

    Article  Google Scholar 

  4. Haenni, R. and Hartmann, S. (2006). Modeling partially reliable information sources: a general approach based on Dempster-Shafer theory. International Journal of Information Fusion, 7(4):361–379.

    Article  Google Scholar 

  5. Haenni, R., Kohlas, J., and Lehmann, N. (2000). Probabilistic argumentation systems. In Gabbay, D. M. and Smets, P., editors, Handbook of Defeasible Reasoning and Uncertainty Management Systems, volume 5: Algorithms for Uncertainty and Defeasible Reasoning, pages 221–288. Kluwer Academic Publishers, Dordrecht, Netherlands.

    Google Scholar 

  6. Haenni, R. and Lehmann, N. (2003). Probabilistic argumentation systems: a new perspective on Dempster-Shafer theory. International Journal of Intelligent Systems, Special Issue on the Dempster-Shafer Theory of Evidence, 18(1):93–106.

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  7. Haenni, R., Romeijn, J., Wheeler, G., and Williamson, J. (2008). Possible semantics for a common framework of probabilistic logics. In Huynh, V. N., editor, UncLog’08, International Workshop on Interval/Probabilistic Uncertainty and Non-Classical Logics, Advances in Soft Computing, Ishikawa, Japan.

    Google Scholar 

  8. Kohlas, J. (2003). Probabilistic argumentation systems: A new way to combine logic with probability. Journal of Applied Logic, 1(3–4):225–253.

    Article  Google Scholar 

  9. Pearl, J. (1990a). Reasoning with belief functions: An analysis of compatibility. International Journal of Approximate Reasoning, 4(5–6):363–389.

    Article  Google Scholar 

  10. Ruspini, E. H. (1986). The logical foundations of evidential reasoning. Technical Report 408, SRI International, AI Center, Menlo Park, USA.

    Google Scholar 

  11. Ruspini, E. H., Lowrance, J., and Strat, T. (1992). Understanding evidential reasoning. International Journal of Approximate Reasoning, 6(3):401–424.

    Article  Google Scholar 

  12. Shafer, G. (1976). A Mathematical Theory of Evidence. Princeton University Press, Princeton, NJ.

    Google Scholar 

  13. Smets, P. and Kennes, R. (1994). The transferable belief model. Artificial Intelligence, 66:191–234.

    Article  Google Scholar 

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Correspondence to Rolf Haenni .

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Haenni, R., Romeijn, JW., Wheeler, G., Williamson, J. (2011). Probabilistic Argumentation. In: Probabilistic Logics and Probabilistic Networks. Synthese Library, vol 350. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-0008-6_3

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