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Henri Prade [36]Henri M. Prade [1]
  1.  54
    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.  35
    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.  6
    Structures of Opposition and Comparisons: Boolean and Gradual Cases.Didier Dubois, Henri Prade & Agnès Rico - 2020 - Logica Universalis 14 (1):115-149.
    This paper first investigates logical characterizations of different structures of opposition that extend the square of opposition in a way or in another. Blanché’s hexagon of opposition is based on three disjoint sets. There are at least two meaningful cubes of opposition, proposed respectively by two of the authors and by Moretti, and pioneered by philosophers such as J. N. Keynes, W. E. Johnson, for the former, and H. Reichenbach for the latter. These cubes exhibit four and six squares of (...)
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  4.  22
    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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  5.  24
    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.  61
    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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  7.  37
    Fuzzy Sets and Systems. Theory and Applications.Didier Dubois & Henri Prade - 1982 - Journal of Symbolic Logic 47 (3):702-703.
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  8.  2
    Epistemic Entrenchment and Possibilistic Logic.Didier Dubois & Henri Prade - 1991 - Artificial Intelligence 50 (2):223-239.
  9.  4
    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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  10.  57
    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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  11.  13
    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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  12.  10
    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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  13. Using Arguments for Making and Explaining Decisions.Leila Amgoud & Henri Prade - 2009 - Artificial Intelligence 173 (3-4):413-436.
  14.  42
    On the Relation Between Possibilistic Logic and Modal Logics of Belief and Knowledge.Mohua Banerjee, Didier Dubois, Lluis Godo & Henri Prade - 2017 - Journal of Applied Non-Classical Logics 27 (3-4):206-224.
    Possibilistic logic and modal logic are knowledge representation frameworks sharing some common features, such as the duality between possibility and necessity, and the decomposability of necessity for conjunctions, as well as some obvious differences since possibility theory is graded. At the semantic level, possibilistic logic relies on possibility distributions and modal logic on accessibility relations. In the last 30 years, there have been a series of attempts for bridging the two frameworks in one way or another. In this paper, we (...)
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  15.  22
    Multiple Agent Possibilistic Logic.Asma Belhadi, Didier Dubois, Faiza Khellaf-Haned & Henri Prade - 2013 - Journal of Applied Non-Classical Logics 23 (4):299-320.
    The paper presents a ‘multiple agent’ logic where formulas are pairs of the form, made of a proposition and a subset of agents. The formula is intended to mean ‘ all agents in believe that is true’. The formal similarity of such formulas with those of possibilistic logic, where propositions are associated with certainty levels, is emphasised. However, the subsets of agents are organised in a Boolean lattice, while certainty levels belong to a totally ordered scale. The semantics of a (...)
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  16. Nonmonotonic Reasoning, Conditional Objects and Possibility Theory.Salem Benferhat, Didier Dubois & Henri Prade - 1997 - Artificial Intelligence 92 (1-2):259-276.
  17. Preferences in AI: An Overview.Carmel Domshlak, Eyke Hüllermeier, Souhila Kaci & Henri Prade - 2011 - Artificial Intelligence 175 (7-8):1037-1052.
  18.  6
    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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  19. Default Reasoning and Possibility Theory.Didier Dubois & Henri Prade - 1988 - Artificial Intelligence 35 (2):243-257.
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  20. Fuzzy Set and Possibility Theory-Based Methods in Artificial Intelligence.Didier Dubois & Henri Prade - 2003 - Artificial Intelligence 148 (1-2):1-9.
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  21. Fuzzy Sets, Logics, and Reasoning About Knowledge.Didier Dubois, Henri M. Prade & E. P. Klement - 1999
     
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  22. Generalized Possibilistic Logic: Foundations and Applications to Qualitative Reasoning About Uncertainty.Didier Dubois, Henri Prade & Steven Schockaert - 2017 - Artificial Intelligence 252:139-174.
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  23.  89
    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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  24. Special Issue of the Journal Artificial Intelligence on “Fuzzy Set and Possibility Theory-Based Methods in Artificial Intelligence”.Didier Dubois & Henri Prade - 2001 - Artificial Intelligence 127 (2):269-270.
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  25. Special Issue of the Journal Artificial Intelligence on “Fuzzy Set and Possibility Theory-Based Methods in Artificial Intelligence”.Didier Dubois & Henri Prade - 2001 - Artificial Intelligence 127 (1):163-164.
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  26. Special Issue of the Journal Artificial Intelligence on “Fuzzy Set and Possibility Theory-Based Methods in Artificial Intelligence”.Didier Dubois & Henri Prade - 2001 - Artificial Intelligence 128 (1-2):245-246.
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  27. The First Steps in Fuzzy Set Theory in France Forty Years Ago.Didier Dubois & Henri Prade - 2017 - Archives for the Philosophy and History of Soft Computing 2017 (1).
    At the occasion of the fiftieth anniversary of the founding article “Fuzzy sets” by L. A. Zadeh, published in 1965, we briefly outline the beginnings of fuzzy set research in France, taking place some ten years later, pointing out the pioneer- ing role of Arnold Kaufmann and few others in this emergence. Moreover, we also point out that the French counterpart of the name “fuzzy set” had appeared some 15 years before Zadeh’s paper, in a paper written in French by (...)
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  28.  34
    Vérité Incertaine? Vérité Approximative.Didier Dubois & Henri Prade - 2001 - Rue Descartes 31 (1):105.
  29.  7
    Weighted Logics for Artificial Intelligence – 2.Lluis Godo, Henri Prade & Guilin Qi - 2015 - Journal of Applied Logic 13 (4):395-396.
  30.  3
    Homogeneous And Heterogeneous Logical Proportions: An Introduction.Franz Guenthner, Dov Gabbay, Gilles Richard & Henri Prade - 2018 - In Dov M. Gabbay & Franz Guenthner (eds.), Handbook of Philosophical Logic: Volume 18. Springer Verlag.
    Commonsense reasoning often relies on the perception of similarity as well as dissimilarity between objects or situations.
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  31.  21
    Reasoning with Imperfect Information and Knowledge.Gabriele Kern-Isberner, Igor Douven, Markus Knauff & Henri Prade - 2017 - Minds and Machines 27 (1):7-9.
  32. 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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  33.  8
    Reasoning in Non-Probabilistic Uncertainty: Logic Programming and Neural-Symbolic Computing as Examples.Henri Prade, Markus Knauff, Igor Douven & Gabriele Kern-Isberner - 2017 - Minds and Machines 27 (1):37-77.
    This article aims to achieve two goals: to show that probability is not the only way of dealing with uncertainty ; and to provide evidence that logic-based methods can well support reasoning with uncertainty. For the latter claim, two paradigmatic examples are presented: logic programming with Kleene semantics for modelling reasoning from information in a discourse, to an interpretation of the state of affairs of the intended model, and a neural-symbolic implementation of input/output logic for dealing with uncertainty in dynamic (...)
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  34.  1
    From the Structures of Opposition Between Similarity and Dissimilarity Indicators to Logical Proportions.Gilles Richard & Henri Prade - 2017 - In Gordana Dodig-Crnkovic & Raffaela Giovagnoli (eds.), Representation of Reality: Humans, Other Living Organism and Intelligent Machines. Springer.
    Comparative thinking plays a key role in our appraisal of reality. Comparing two objects or situations A and B, described in terms of Boolean features, may involve four basic similarity or dissimilarity indicators referring to what A and B have in common, or to what is particular to A or particular to B. These four indicators are naturally organized into a cube of opposition, which includes two classical squares of opposition, as well as other noticeable squares. From the knowledge of (...)
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  35. Interpolative and Extrapolative Reasoning in Propositional Theories Using Qualitative Knowledge About Conceptual Spaces.Steven Schockaert & Henri Prade - 2013 - Artificial Intelligence 202:86-131.
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  36. Solving Conflicts in Information Merging by a Flexible Interpretation of Atomic Propositions.Steven Schockaert & Henri Prade - 2011 - Artificial Intelligence 175 (11):1815-1855.
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  37. Introducing Possibilistic Logic in ILP for Dealing with Exceptions.Mathieu Serrurier & Henri Prade - 2007 - Artificial Intelligence 171 (16-17):939-950.
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