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Henri Prade [18]Henri M. Prade [1]
  1.  14
    From Blanché's Hexagonal Organization of Concepts to Formal Concept Analysis and Possibility Theory.Didier Dubois & Henri Prade - 2012 - Logica Universalis 6 (1-2):149-169.
    The paper first introduces a cube of opposition that associates the traditional square of opposition with the dual square obtained by Piaget’s reciprocation. It is then pointed out that Blanché’s extension of the square-of-opposition structure into an conceptual hexagonal structure always relies on an abstract tripartition. Considering quadripartitions leads to organize the 16 binary connectives into a regular tetrahedron. Lastly, the cube of opposition, once interpreted in modal terms, is shown to account for a recent generalization of formal concept analysis, (...)
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  2.  25
    Some Syntactic Approaches to the Handling of Inconsistent Knowledge Bases: A Comparative Study Part 1: The Flat Case.Salem Benferhat, Didier Dubois & Henri Prade - 1997 - Studia Logica 58 (1):17-45.
    This paper presents and discusses several methods for reasoning from inconsistent knowledge bases. A so-called argued consequence relation, taking into account the existence of consistent arguments in favour of a conclusion and the absence of consistent arguments in favour of its contrary, is particularly investigated. Flat knowledge bases, i.e., without any priority between their elements, are studied under different inconsistency-tolerant consequence relations, namely the so-called argumentative, free, universal, existential, cardinality-based, and paraconsistent consequence relations. The syntax-sensitivity of these consequence relations is (...)
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  3.  2
    Accepted Beliefs, Revision and Bipolarity in the Possibilistic Framework.Didier Dubois & Henri Prade - 2009 - In Franz Huber & Christoph Schmidt-Petri (eds.), Degrees of Belief. Springer. pp. 161--184.
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  4. Possibility Theory, Probability Theory, and Muliple-Valued Logics: A Clarification.Henri Prade & D. Dubois - 2001 - Annals of Mathematics and Artificial Intelligence 32:35--66.
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  5.  4
    The Strength of Desires: A Logical Approach.Didier Dubois, Emiliano Lorini & Henri Prade - 2017 - Minds and Machines 27 (1):199-231.
    The aim of this paper is to propose a formal approach to reasoning about desires, understood as logical propositions which we would be pleased to make true, also acknowledging the fact that desire is a matter of degree. It is first shown that, at the static level, desires should satisfy certain principles that differ from those to which beliefs obey. In this sense, from a static perspective, the logic of desires is different from the logic of beliefs. While the accumulation (...)
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  6.  31
    A Practical Approach to Revising Prioritized Knowledge Bases.Salem Benferhat, Didier Dubois, Henri Prade & Mary-Anne Williams - 2002 - Studia Logica 70 (1):105-130.
    This paper investigates simple syntactic methods for revising prioritized belief bases, that are semantically meaningful in the frameworks of possibility theory and of Spohn''s ordinal conditional functions. Here, revising prioritized belief bases amounts to conditioning a distribution function on interpretations. The input information leading to the revision of a knowledge base can be sure or uncertain. Different types of scales for priorities are allowed: finite vs. infinite, numerical vs. ordinal. Syntactic revision is envisaged here as a process which transforms a (...)
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  7.  48
    Knowledge-Driven Versus Data-Driven Logics.Didier Dubois, Petr Hájek & Henri Prade - 2000 - Journal of Logic, Language and Information 9 (1):65--89.
    The starting point of this work is the gap between two distinct traditions in information engineering: knowledge representation and data - driven modelling. The first tradition emphasizes logic as a tool for representing beliefs held by an agent. The second tradition claims that the main source of knowledge is made of observed data, and generally does not use logic as a modelling tool. However, the emergence of fuzzy logic has blurred the boundaries between these two traditions by putting forward fuzzy (...)
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  8.  3
    Reasoning with Imperfect Information and Knowledge.Gabriele Kern-Isberner, Igor Douven, Markus Knauff & Henri Prade - 2017 - Minds and Machines 27 (1):7-9.
  9.  74
    New Trends and Open Problems in Fuzzy Logic and Approximate Reasoning.Henri Prade - 1996 - Theoria 11 (3):109-121.
    This short paper about fuzzy set-based approximate reasoning first emphasizes the three main semantics for fuzzy sets: similarity, preference and uncertainty. The difference between truth-functional many-valued logics of vague or gradual propositions and non fully compositional calculi such as possibilistic logic (which handles uncertainty) or similarity logics is stressed. Then, potentials of fuzzy set-based reasoning methods are briefly outlined for various kinds of approximate reasoning: deductive reasoning about flexible constraints, reasoning under uncertainty and inconsistency, hypothetical reasoning, exception-tolerant plausible reasoning using (...)
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  10.  15
    Vérité incertaine – Vérité approximative.Didier Dubois & Henri Prade - 2001 - Rue Descartes 1 (1):105-126.
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  11.  12
    Multiple Agent Possibilistic Logic.Asma Belhadi, Didier Dubois, Faiza Khellaf-Haned & Henri Prade - 2013 - Journal of Applied Non-Classical Logics 23 (4):299-320.
  12.  2
    Vérité Incertaine? Vérité Approximative.Didier Dubois & Henri Prade - 2001 - Rue Descartes 31 (1):105.
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  13.  5
    From Analogical Proportion to Logical Proportions.Henri Prade & Gilles Richard - 2013 - Logica Universalis 7 (4):441-505.
    Given a 4-tuple of Boolean variables (a, b, c, d), logical proportions are modeled by a pair of equivalences relating similarity indicators ( \({a \wedge b}\) and \({\overline{a} \wedge \overline{b}}\) ), or dissimilarity indicators ( \({a \wedge \overline{b}}\) and \({\overline{a} \wedge b}\) ) pertaining to the pair (a, b), to the ones associated with the pair (c, d). There are 120 semantically distinct logical proportions. One of them models the analogical proportion which corresponds to a statement of the form “a (...)
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  14.  4
    Comparing Decisions on the Basis of a Bipolar Typology of Arguments.Leila Amgoud & Henri Prade - 2008 - In Giacomo Della Riccia, Didier Dubois & Hans-Joachim Lenz (eds.), Preferences and Similarities. Springer. pp. 249--264.
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  15.  2
    Inference in Possibilistic Hypergraphs.Didier Dubois & Henri Prade - 1991 - In B. Bouchon-Meunier, R. R. Yager & L. A. Zadeh (eds.), Uncertainty in Knowledge Bases. Springer. pp. 249--259.
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  16. A Glance at Non-Standard Models and Logics of Uncertainty and Vagueness.Didier Dubois & Henri Prade - 1993 - In J. Dubucs (ed.), Philosophy of Probability. Kluwer, Dordrecht. pp. 169--222.
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  17. Fuzzy Sets, Logics, and Reasoning About Knowledge.Didier Dubois, Henri M. Prade & E. P. Klement - 1999
     
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  18. New Trends and Open Problems in Fuzzy Logic and Approximate Reasoning.Didier Dubois & Henri Prade - 1996 - Theoria: Revista de Teoría, Historia y Fundamentos de la Ciencia 11 (3):109-121.
    This short paper about fuzzy set-based approximate reasoning first emphasizes the three main semantics for fuzzy sets: similarity, preference and uncertainty. The difference between truth-functional many-valued logics of vague or gradual propositions and non fully compositional calculi such as possibilistic logic or similarity logics is stressed. Then, potentials of fuzzy set-based reasoning methods are briefly outlined for various kinds of approximate reasoning: deductive reasoning about flexible constraints, reasoning under uncertainty and inconsistency, hypothetical reasoning, exception-tolerant plausible reasoning using generic knowledge, interpolative (...)
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  19. Weighted Logics for Artificial Intelligence – 2.Lluis Godo, Henri Prade & Guilin Qi - 2015 - Journal of Applied Logic 13 (4):395-396.