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Documents authored by Dubois, Didier

Document
DOI: 10.4230/DagSemProc.04271.9
Representing preferences in the possibilistic setting

Authors: Souhila Kaci, Didier Dubois, and Henri Prade

Published in: Dagstuhl Seminar Proceedings, Volume 4271, Preferences: Specification, Inference, Applications (2006)

Abstract
The accurate and easy representation of users' preferences in information engineering systems becomes an important issue. Possibility theory provides a generic framework for the qualitative representation of preferences, where several equivalent information formats co- exist (distribution, logical bases, conditionals, graphical networks). Moreover, a bipolar representation distinguishing between positive and negative preferences has been developed in this setting. The paper offers a comprehensive survey of these representation issues.
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Souhila Kaci, Didier Dubois, and Henri Prade. Representing preferences in the possibilistic setting. In Preferences: Specification, Inference, Applications. Dagstuhl Seminar Proceedings, Volume 4271, pp. 1-9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)

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@InProceedings{souhilakaci_et_al:DagSemProc.04271.9,
  author =	{Souhila Kaci and Dubois, Didier and Prade, Henri},
  title =	{{Representing preferences in the possibilistic setting}},
  booktitle =	{Preferences: Specification, Inference, Applications},
  pages =	{1--9},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2006},
  volume =	{4271},
  editor =	{Gianni Bosi and Ronen I. Brafman and Jan Chomicki and Werner Kie{\ss}ling},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.04271.9},
  URN =		{urn:nbn:de:0030-drops-4063},
  doi =		{10.4230/DagSemProc.04271.9},
  annote =	{Keywords: Possibility, preference, possibilistic logic}
}
Document
DOI: 10.4230/DagSemProc.05321.1
05321 – Panel on belief change

Authors: Isaac Levi, Giacomo Bonanno, Bernard Walliser, Didier Dubois, Hans Rott, James Delgrande, and Jérôme Lang

Published in: Dagstuhl Seminar Proceedings, Volume 5321, Belief Change in Rational Agents: Perspectives from Artificial Intelligence, Philosophy, and Economics (2005)

Abstract
This document gathers the panelists' contribution.
Cite as

Isaac Levi, Giacomo Bonanno, Bernard Walliser, Didier Dubois, Hans Rott, James Delgrande, and Jérôme Lang. 05321 – Panel on belief change. In Belief Change in Rational Agents: Perspectives from Artificial Intelligence, Philosophy, and Economics. Dagstuhl Seminar Proceedings, Volume 5321, pp. 1-12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2005)

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@InProceedings{levi_et_al:DagSemProc.05321.1,
  author =	{Levi, Isaac and Bonanno, Giacomo and Walliser, Bernard and Dubois, Didier and Rott, Hans and Delgrande, James and Lang, J\'{e}r\^{o}me},
  title =	{{05321 – Panel on belief change}},
  booktitle =	{Belief Change in Rational Agents: Perspectives from Artificial Intelligence, Philosophy, and Economics},
  pages =	{1--12},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2005},
  volume =	{5321},
  editor =	{James Delgrande and Jerome Lang and Hans Rott and Jean-Marc Tallon},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.05321.1},
  URN =		{urn:nbn:de:0030-drops-3580},
  doi =		{10.4230/DagSemProc.05321.1},
  annote =	{Keywords: Belief revision, iterated belief revision, update, merging, dynamic logic, possibility theory, conditionals, social choice, distance, complexity}
}
Document
DOI: 10.4230/DagSemProc.05321.13
Probabilistic Abduction Without Priors

Authors: Didier Dubois, Angelo Gilio, and Gabriele Kern-Isberner

Published in: Dagstuhl Seminar Proceedings, Volume 5321, Belief Change in Rational Agents: Perspectives from Artificial Intelligence, Philosophy, and Economics (2005)

Abstract
This paper considers the simple problem of abduction in the framework of Bayes theorem, i.e. computing a posterior probability of an hypothesis when its prior probability is not available, either because there are no statistical data on which to rely on, or simply because a human expert is reluctant to provide a subjective assessment of this prior probability. The problem remains an open issue since a simple sensitivity analysis on the value of the unknown prior yields empty results. This paper tries to survey and comment on various solutions to this problem: the use of likelihood functions (as in classical statistics), the use of information principles like maximal entropy, Shapley value, maximum likelihood. We also study the problem in the setting of de Finetti coherence approach, which does not exclude conditioning on contingent events with zero probability. We show that the ad hoc likelihood function method, that can be reinterpreted in terms of possibility theory, is consistent with most other formal approaches. However, the maximal entropy solution is significantly different.
Cite as

Didier Dubois, Angelo Gilio, and Gabriele Kern-Isberner. Probabilistic Abduction Without Priors. In Belief Change in Rational Agents: Perspectives from Artificial Intelligence, Philosophy, and Economics. Dagstuhl Seminar Proceedings, Volume 5321, pp. 1-8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2005)

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@InProceedings{dubois_et_al:DagSemProc.05321.13,
  author =	{Dubois, Didier and Gilio, Angelo and Kern-Isberner, Gabriele},
  title =	{{Probabilistic Abduction Without Priors}},
  booktitle =	{Belief Change in Rational Agents: Perspectives from Artificial Intelligence, Philosophy, and Economics},
  pages =	{1--8},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2005},
  volume =	{5321},
  editor =	{James Delgrande and Jerome Lang and Hans Rott and Jean-Marc Tallon},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.05321.13},
  URN =		{urn:nbn:de:0030-drops-3286},
  doi =		{10.4230/DagSemProc.05321.13},
  annote =	{Keywords: Conditional probability, Bayes Theorem, imprecise probability, entropy, possibility theory, maximum likelihood}
}
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