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Didier Dubois

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77 papers
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77

IJCAI Conference 2025 Conference Paper

40 Years of Research in Possibilistic Logic – a Survey

  • Didier Dubois
  • Henri Prade

Possibilistic logic is forty years old. Possibilistic logic is a logic that handles classical logic formulas associated with weights taking values in a linearly ordered set or more generally in a lattice. Over the decades, possibilistic logic has undergone numerous developments at both theoretical and applied levels. The ambition of this article is to review all these developments while exposing the main ideas behind them.

JELIA Conference 2019 Invited Paper

Possibilistic Logic: From Certainty-Qualified Statements to Two-Tiered Logics - A Prospective Survey

  • Didier Dubois
  • Henri Prade

Abstract Possibilistic logic (PL) is more than thirty years old. The paper proposes a survey of its main developments and applications in artificial intelligence, together with a short presentation of works in progress. PL amounts to a classical logic handling of certainty-qualified statements. Certainty is estimated in the setting of possibility theory as a lower bound of a necessity set-function. An elementary possibilistic formula is a pair made of a classical logic formula, and a certainty level belonging to a bounded scale. Basic PL handles only conjunctions of such formulas, and PL bases can be viewed as classical logic bases layered in terms of certainty. Semantics is in terms of epistemic states represented by fuzzy sets of interpretations. A PL base is associated with an inconsistency level above which formulas are safe from inconsistency. Applications include reasoning with default rules, belief revision, Bayesian possibilistic networks, information fusion, and preference modeling (in this latter case, certainty is turned into priority). Different extensions of basic PL are briefly reviewed, where levels take values in lattices, are replaced by vectors of levels, or are handled in a purely symbolic manner (without being instantiated). This latter extension may be of interest for explanation purposes. A paraconsistent treatment of inconsistency is also discussed. Still another extension allows for associating possibilistic formulas with sets of agents or sources that support them. In generalized possibilistic logic (GPL), negation and disjunction can be applied as well as conjunction, to possibilistic formulas. It may be viewed as a fragment of modal logic (such as KD45) where modalities cannot be nested. GPL can be still extended to a logic involving both objective and non-nested multimodal formulas. Applications of GPL to the modeling of ignorance, to the representation of answer set programs, to reasoning about other agents’ beliefs, and to a logic of argumentation are outlined. Generally speaking, the interest and the strength of PL relies on a sound alliance between classical logic and possibility theory which offers a rich representation setting allowing an accurate modeling of partial ignorance. The paper focuses more on ideas than on technicalities and provides references for details (Invited talk presented by the second author).

FLAP Journal 2018 Journal Article

Possibilistic Reasoning from Partially Ordered Belief Bases with the Sure Thing Principle.

  • Claudette Cayrol
  • Didier Dubois
  • Fayçal Touazi

We consider the problem of reasoning from logical bases equipped with a partial order expressing relative certainty, with a view to construct a partially ordered deductive closure via syntactic inference. At the syntactic level we use a language expressing pairs of related formulas and axioms describing the properties of the order. Reasoning about uncertainty using possibility theory relies on the idea that if an agent believes each among two propositions to some extent, then this agent should believe their conjunction to the same extent. This principle is known as adjunction. Adjunction is often accepted in epistemic logic but fails with probabilistic reasoning. In the latter, another principle prevails, namely the sure thing principle, that claims that the certainty ordering between propositions should be invariant to the addition or deletion of possible worlds common to both sets of models of these propositions. Pursuing our work on relative certainty logic based on possibility theory, we propose a qualitative likelihood logic that respects the sure thing principle, albeit using a likelihood relation that preserves adjunction.

ECAI Conference 2016 Conference Paper

Preference Modeling with Possibilistic Networks and Symbolic Weights: A Theoretical Study

  • Nahla Ben Amor
  • Didier Dubois
  • Héla Gouider
  • Henri Prade

The use of possibilistic networks for representing conditional preference statements on discrete variables has been proposed only recently. The approach uses non-instantiated possibility weights to define conditional preference tables. Moreover, additional information about the relative strengths of these symbolic weights can be taken into account. The fact that at best we have some information about the relative values of these weights acknowledges the qualitative nature of preference specification. These conditional preference tables give birth to vectors of symbolic weights that reflect the preferences that are satisfied and those that are violated in a considered situation. The comparison of such vectors may rely on different orderings: the ones induced by the product-based, or the minimum-based chain rule underlying the possibilistic network, the discrimin, or leximin refinements of the minimum-based ordering, as well as Pareto ordering, and the symmetric Pareto ordering that refines it. A thorough study of the relations between these orderings in presence of vector components that are symbolic rather numerical is presented. In particular, we establish that the product-based ordering and the symmetric Pareto ordering coincide in presence of constraints comparing pairs of symbolic weights. This ordering agrees in the Boolean case with the inclusion between the sets of preference statements that are violated. The symmetric Pareto ordering may be itself refined by the leximin ordering. The paper highlights the merits of product-based possibilistic networks for representing preferences and provides a comparative discussion with CP-nets and OCF-networks.

IJCAI Conference 2015 Conference Paper

The Cube of Opposition: A Structure Underlying Many Knowledge Representation Formalisms

  • Didier Dubois
  • Henri Prade
  • Agn
  • egrave; s Rico

The square of opposition is a structure involving two involutive negations and relating quantified statements, invented in Aristotle time. Rediscovered in the second half of the XXth century, and advocated as being of interest for understanding conceptual structures and solving problems in paraconsistent logics, the square of opposition has been recently completed into a cube, which corresponds to the introduction of a third negation. Such a cube can be encountered in very different knowledge representation formalisms, such as modal logic, possibility theory in its all-or-nothing version, formal concept analysis, rough set theory and abstract argumentation. After restating these results in a unified perspective, the paper proposes a graded extension of the cube and shows that several qualitative, as well as quantitative formalisms, such as Sugeno integrals used in multiple criteria aggregation and qualitative decision theory, or yet belief functions and Choquet integrals, are amenable to transformations that form graded cubes of opposition. This discovery leads to a new perspective on many knowledge representation formalisms, laying bare their underlying common features. The cube of opposition exhibits fruitful parallelisms between different formalisms, which leads to highlight some missing components present in one formalism and currently absent from another.

ECAI Conference 2014 Conference Paper

Reasoning about Uncertainty and Explicit Ignorance in Generalized Possibilistic Logic

  • Didier Dubois
  • Henri Prade
  • Steven Schockaert

Generalized possibilistic logic (GPL) is a logic for reasoning about the revealed beliefs of another agent. It is a two-tier propositional logic, in which propositional formulas are encapsulated by modal operators that are interpreted in terms of uncertainty measures from possibility theory. Models of a GPL theory represent weighted epistemic states and are encoded as possibility distributions. One of the main features of GPL is that it allows us to explicitly reason about the ignorance of another agent. In this paper, we study two types of approaches for reasoning about ignorance in GPL, based on the idea of minimal specificity and on the notion of guaranteed possibility, respectively. We show how these approaches naturally lead to different flavours of the language of GPL and a number of decision problems, whose complexity ranges from the first to the third level of the polynomial hierarchy.

AAAI Conference 2014 Conference Paper

Structured Possibilistic Planning Using Decision Diagrams

  • Nicolas Drougard
  • Florent Teichteil-Königsbuch
  • Jean-Loup Farges
  • Didier Dubois

Qualitative Possibilistic Mixed-Observable MDPs (π- MOMDPs), generalizing π-MDPs and π-POMDPs, are well-suited models to planning under uncertainty with mixed-observability when transition, observation and reward functions are not precisely known and can be qualitatively described. Functions defining the model as well as intermediate calculations are valued in a finite possibilistic scale L, which induces a finite belief state space under partial observability contrary to its probabilistic counterpart. In this paper, we propose the first study of factored π-MOMDP models in order to solve large structured planning problems under qualitative uncertainty, or considered as qualitative approximations of probabilistic problems. Building upon the SPUDD algorithm for solving factored (probabilistic) MDPs, we conceived a symbolic algorithm named PPUDD for solving factored π-MOMDPs. Whereas SPUDD’s decision diagrams’ leaves may be as large as the state space since their values are real numbers aggregated through additions and multiplications, PPUDD’s ones always remain in the finite scale L via min and max operations only. Our experiments show that PPUDD’s computation time is much lower than SPUDD, Symbolic-HSVI and APPL for possibilistic and probabilistic versions of the same benchmarks under either total or mixed observability, while still providing high-quality policies.

UAI Conference 2013 Conference Paper

Qualitative Possibilistic Mixed-Observable MDPs

  • Nicolas Drougard
  • Florent Teichteil-Königsbuch
  • Jean-Loup Farges
  • Didier Dubois

Possibilistic and qualitative POMDPs (π- POMDPs) are counterparts of POMDPs used to model situations where the agent’s initial belief or observation probabilities are imprecise due to lack of past experiences or insufficient data collection. However, like probabilistic POMDPs, optimally solving π- POMDPs is intractable: the finite belief state space exponentially grows with the number of system’s states. In this paper, a possibilistic version of Mixed-Observable MDPs is presented to get around this issue: the complexity of solving π-POMDPs, some state variables of which are fully observable, can be then dramatically reduced. A value iteration algorithm for this new formulation under infinite horizon is next proposed and the optimality of the returned policy (for a specified criterion) is shown assuming the existence of a ”stay” action in some goal states. Experimental work finally shows that this possibilistic model outperforms probabilistic POMDPs commonly used in robotics, for a target recognition problem where the agent’s observations are imprecise.

KR Conference 2012 Conference Paper

A Bipolar Framework for Combining Beliefs about Vague Propositions

  • Jonathan Lawry
  • Didier Dubois

short. For propositions involving vague concepts this naturally results in truth-gaps. In other words, there are cases in which a proposition is neither absolutely true nor absolutely false but instead borderline. The fundamental idea of this paper is that such truth-gaps can be exploited to provide additional flexibility when combining different, and possibly inconsistent, viewpoints and beliefs in order to achieve consensus. In particular, two inconsistent points of view each giving different truth values to a certain proposition p might be combined into a compromise position in which p is a borderline case. We illustrate this idea with a simple example as follows: Consider a scenario in which two agents a1 and a2 need to agree about the truth or falsity of the proposition p =‘UK inflation is currently low’. Now the truth of p is principally dependent on two factors; the actual level of UK inflation f as a percentage, and the definition of the predicate low when applied to inflation rates. In this example we can view f as being objectively known to both a1 and a2 having been established by using generally accepted economic metrics, while the definition of low in this context is subjective and may differ between a1 and a2. For instance, one possible bipolar model of low could be as follows: Suppose each agent ai defines lower and upper thresholds f i ≤ f i on percentages so that, for them, p is absolutely true if f ≤ f i, absolutely false if f > f i and borderline if f i < f ≤ f i. Now further suppose that initially f 1 < f 1 < f ≤ f 2 < f 2 and hence a1 views p as being absolutely false, while a2 views it as being absolutely true. One way in which a1 and a2 can adapt their viewpoints to a obtain a common position would be for a1 to increase their upper threshold so that f 1 ≥ f and for a2 to decrease their lower threshold so that f 2 < f thus allowing both a1 and a2 to agree on a truth valuation of borderline for p. The adequate representation of epistemic uncertainty is of central importance in any effective model of belief. Typically we think of uncertainty as arising because of insufficient information about the state of the world. However, in the presence of vagueness there may also be semantic uncertainty due to our partial knowledge of language conventions. For instance, in the above example each agent may be uncertain about the truth-value of p for the following reasons: There may be some doubt as to the actual current inflation A bipolar framework is introduced for combining agents’ beliefs so as to enable them to reach a common shared position or viewpoint. Our approach exploits the truth-gaps inherent to propositions involving vague concepts, by allowing agents to soften directly conflicting opinions. To this end we adopt a bipolar truth-model for propositional logic characterised by lower and upper valuations on the sentences of the language. According to this model sentences may be absolutely true, absolutely false or borderline (i. e. neither absolutely true nor absolutely false). The added flexibility of a possible truth-gap between absolutely true and absolutely false allows agents with inconsistent viewpoints, in which a proposition p is absolutely true according to one view and absolutely false according to the other, to reach a compromise position in which p is borderline. Within this framework four combination operators are proposed for combining different viewpoints as represented by different valuation pairs. Intuitively, these correspond to compromise positions with different levels of semantic precision (or vagueness). Kleene belief pairs are then introduced as lower and upper measures quantifying epistemic uncertainty about the sentences of the language when valuation pairs provide the underlying truth model. The combination operators on valuation pairs are then extended to belief pairs using a general schema incorporating a probabilistic model of the interaction between agents. The properties of the four operators are then investigated within this extended framework.

ECAI Conference 2012 Conference Paper

Decision-making with Sugeno integrals: DMU vs. MCDM

  • Miguel Couceiro
  • Didier Dubois
  • Henri Prade
  • Tamás Waldhauser

This paper clarifies the connection between multiple criteria decision-making and decision under uncertainty in a qualitative setting relying on a finite value scale. While their mathematical formulations are very similar, the underlying assumptions differ and the latter problem turns out to be a special case of the former. Sugeno integrals are very general aggregation operations that can represent preference relations between uncertain acts or between multifactorial alternatives where attributes share the same totally ordered domain. This paper proposes a generalized form of the Sugeno integral that can cope with attributes which have distinct domains via the use of qualitative utility functions. In the case of decision under uncertainty, this model corresponds to state-dependent preferences on act consequences. Axiomatizations of the corresponding preference functionals are proposed in the cases where uncertainty is represented by possibility measures, by necessity measures, and by general monotonic set-functions, respectively. This is achieved by weakening previously proposed axiom systems for Sugeno integrals.

KR Conference 2012 Conference Paper

Stable models in generalized possibilistic logic

  • Didier Dubois
  • Henri Prade
  • Steven Schockaert

An important aspect of possibilistic logic is that models correspond to epistemic states, rather than to propositional interpretations, which forms a natural basis for epistemic reasoning. However, possibilistic logic only takes sets of formulas of the form (α, λ) into account, which we could interpret as conjunctions of assertions of the form N (α) ≥ λ. In some applications, on the other hand, we may want to link such assertions using different propositional connectives. In logic programming, for instance, a (negation-free) rule such as α → β intuitively means that whenever α is known to be true, we should accept β to be true as well. This could be expressed using necessity measures and material implication as N (α) ≥ 1 ⇒ N (β) ≥ 1. This implication, however, cannot be expressed in possibilistic logic, an observation which stands in stark contrast to the expressivity of modal logics for epistemic reasoning. In (Banerjee and Dubois 2009), a so-called Meta-Epistemic Logic (MEL) was introduced as a first step to bridge this gap, in the form of a simple modal logic with a semantics in terms of Boolean possibility distributions (i. e. possibility distributions π such that π(ω) ∈ {0, 1} for every ω ∈ Ω). Essentially, MEL is a fragment of the modal logic KD, in which neither the nesting of modalities nor the occurrence of nonmodal propositional formulas is allowed. Recently, generalized possibilistic logic (GPL) was introduced as a graded version of MEL (Dubois, Prade, and Schockaert 2011; Dubois and Prade 2011), developing an original proposal of (Dubois and Prade 2007). Possibilistic logic is a well-known logic for reasoning under uncertainty, which is based on the idea that the epistemic state of an agent can be modeled by assigning to each possible world a degree of possibility, taken from a totally ordered, but essentially qualitative scale. Recently, a generalization has been proposed that extends possibilistic logic to a meta-epistemic logic, endowing it with the capability of reasoning about epistemic states, rather than merely constraining them. In this paper, we further develop this generalized possibilistic logic (GPL). We introduce an axiomatization showing that GPL is a fragment of a graded version of the modal logic KD, and we prove soundness and completeness w. r. t. a semantics in terms of possibility distributions. Next, we reveal a close link between the wellknown stable model semantics for logic programming and the notion of minimally specific models in GPL. More generally, we analyze the relationship between the equilibrium logic of Pearce and GPL, showing that GPL can essentially be seen as a generalization of equilibrium logic, although its notion of minimal specificity is slightly more demanding than the notion of minimality underlying equilibrium logic.

JELIA Conference 2012 Conference Paper

Three-Valued Logics for Incomplete Information and Epistemic Logic

  • Davide Ciucci
  • Didier Dubois

Abstract There are several three-valued logical systems. They give the impression of a scattered landscape. The majority of the works on this subject gives the truth tables, sometimes an Hilbert style axiomatization in a basic propositional language and a completeness theorem with respect to those truth tables. We show that all the reasonable connectives in three-valued logics can be built starting from few of them. Nevertheless, the issue of the usefulness of each system in relation with the third truth value is often neglected. Here, we review the interpretations of the third truth value. Then, we focus on the unknown case, suggested by Kleene. We show that any formula in three-valued logics can be encoded as a fragment of an epistemic logic (formulae of modal depth 1, with modalities in front of literals), preserving all tautologies and inference rules. We study in particular, the translation of Kleene, Gödel, Łukasiewicz and Nelson logics. This work enables us to lay bare the limited expressive power of three-valued logics in uncertainty management.

ECAI Conference 2006 Conference Paper

Background Default Knowledge and Causality Ascriptions

  • Jean-François Bonnefon
  • Rui Da Silva Neves
  • Didier Dubois
  • Henri Prade

A model is defined that predicts an agent's ascriptions of causality (and related notions of facilitation and justification) between two events in a chain, based on background knowledge about the normal course of the world. Background knowledge is represented by nonmonotonic consequence relations. This enables the model to handle situations of poor information, where background knowledge is not accurate enough to be represented in, e. g. , structural equations. Tentative properties of causality ascriptions are explored, i. e. , preference for abnormal factors, transitivity, coherence with logical entailment, and stability with respect to disjunction and conjunction. Empirical data are reported to support the psychological plausibility of our basic definitions.

KR Conference 2006 Conference Paper

Iterated revision as prioritized merging

  • James Delgrande
  • Didier Dubois
  • Jerome Lang

Standard accounts of iterated belief revision assume a static world, about which an agent receives a sequence of observations. More recent items are assumed to have priority over less recent items. We argue that there is no reason, given a static world, for giving priority to more recent items. Instead we suggest that a sequence of observations should be merged with the agent's beliefs. Since observations may have differing reliability, arguably the appropriate belief change operator is prioritized merging. We develop this view here, suggesting postulates for prioritized merging, and examining existing merging operators with respect to these postulates. As well, we examine other suggested postulates for iterated revision, to determine how well they fit with the prioritized merging interpretation. All postulates for iterated revision that we examine, except for Darwiche and Pearl's controversial C2, are consequences of our suggested postulates for prioritized merging.

KR Conference 2006 Conference Paper

Probabilistic abduction without priors

  • Gabriele Kern-Isberner
  • Didier Dubois
  • Angelo Gilio

This paper considers the simple problem of abduction in the framework of Bayes theorem, when the prior probability of the hypothesis is not available, either because there are no statistical data to rely on, or simply because a human expert is reluctant to provide a subjective assessment of this prior probability. This abduction problem remains an open issue since a simple sensitivity analysis on the value of the unknown prior yields empty results. This paper tries to propose some criteria a solution to this problem should satisfy. It then surveys and comments on various existing or new solutions to this problem: the use of likelihood functions (as in classical statistics), the use of information principles like maximum entropy, Shapley value, maximum likelihood. The formal setting includes de Finetti’s 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 maximum entropy solution is significantly different, despite some formal analogies.

KR Conference 2006 Conference Paper

Qualitative decision making with bipolar information

  • Didier Dubois
  • Helene Fargier

Decisions can be evaluated by sets of positive and negative arguments — the problem is then to compare these sets. Studies in psychology have shown that in this case the scale of evaluation of decisions is generally bipolar. Moreover decisions are often made on the basis of an ordinal ranking of the arguments rather than on a genuine numerical evaluation of their degrees of attractiveness or rejection, hence the qualitative nature of the decision process in practice. In this paper, assuming bipolarity of evaluations and qualitative ratings, we present and axiomatically characterise two decision rules based on possibilistic order of magnitude reasoning that are capable of handling positive and negative affects. They are extensions of the maximin and maximax criteria to the bipolar case. A bipolar extension of possibility theory is thus obtained. In order to overcome the lack of discrimination power of the decision rules, refinements are also proposed, capturing both the efficiency principle and the idea of order of magnitude reasoning.

AILAW Journal 2006 Journal Article

Suitable Properties for Any Electronic Voting System

  • Jean-Luc Koning
  • Didier Dubois

Abstract Numerous countries are heading toward digital infrastructures. In particular this new technology promises to help support methods for elections. However, one should be careful that such an infrastructure does not hinder the voting and representation issues. On the contrary, it should support those issues and help citizens have a clearer picture of the underlying mechanisms. This paper deals with the limits of voting procedures as they are described in classical collective choice theory and reflects on ways to aggregate electronic votes stemming from various individuals that would be at the same time democratic, decisive and rational which is not feasible when candidate rankings alone are taken into account. This paper shows how electronic voting procedures could improve the situation by introducing preference-based votes.

IJCAI Conference 2005 Conference Paper

Minimizing a Makespan Under Uncertainty

  • Jérôme Fortin
  • Pawel Zielinski
  • Didier Dubois
  • Hélène

This paper reconsiders the most basic scheduling problem, that of minimizing the makespan of a partially ordered set of activities, in the context of incomplete knowledge. After positioning this paper in the scope of temporal networks under uncertainty, we provide a complete solution to the problem of finding floats of activities, and of locating surely critical ones, as they are often isolated. The minimal float problem is NP-hard while the maximal float problem is polynomial. New complexity results and efficient algorithms are provided for the interval-valued makespan minimization problem.

TIME Conference 2004 Conference Paper

A Possibility Theory-based Approach for Handling of Uncertain Relations Between Temporal Points

  • Allel HadjAli
  • Didier Dubois
  • Henri Prade

Uncertain relations between temporal points are represented by means of possibility distributions over the three basic relations "smaller than", "equal to", and "greater than". Operations for computing inverse relations, for composing relations, for combining relations coming from different sources and pertaining to the same temporal points, or for representing negative information, are defined. An illustrative example of representing and reasoning with uncertain temporal relations is given. This paper shows how possibilistic temporal uncertainty can be handled in the setting of point algebra. Moreover, the paper emphasizes the advantages of the possibilistic approach over a probabilistic approach previously proposed. This work does for the temporal point algebra what the authors previously did for the temporal interval algebra.

UAI Conference 2004 Conference Paper

A Unified framework for Order-of-magnitude Confidence Relations

  • Didier Dubois
  • Hélène Fargier

The aim of this work is to provide a unified framework for ordinal representations of uncertainty lying at the crosswords between possibility and probability theories. Such confidence relations between events are commonly found in monotonic reasoning, inconsistency management, or qualitative decision theory. They start either from probability theory, making it more qualitative, or from possibility theory, making it more expressive. We show these two trends converge to a class of genuine probability theories. We provide characterization results for these useful tools that preserve the qualitative nature of possibility rankings, while enjoying the power of expressivity of additive representations.

NMR Workshop 2004 Conference Paper

Ordinal and absolute representations of positive information in possibilistic logic

  • Didier Dubois
  • Souhila Kaci
  • Henri Prade

There are two readings of a possibility distribution, a representation format which is useful for encoding uncertain knowledge or preferences. The negative information reading, based on possibility and necessity measures, emphasizes the fact that some interpretations are impossible, or at least have an upper-bounded possibility level. The positive information reading points out that possibility degrees are lower bounded, and thus that some interpretations have non-zero (guaranteed) possibility degrees. This paper provides technical results for the positive view, showing how sets of absolute, or relative, constraints expressed in terms of guaranteed possibility measures can be represented in terms of a possibility distribution. Using previously obtained results for the “negative interpretation side”, it enables us to jointly handle upper and lower logical specifications of a possibility distribution on partitions of the set of interpretations, as pointed out in the conclusion.

IJCAI Conference 2003 Conference Paper

Categorizing classes of signals by means of fuzzy gradual rules

  • Sylvie Galichet
  • Didier Dubois
  • Henri Prade

This paper presents an approach to the approximate description of univariate real-valued functions in terms of precise or imprecise reference points and interpolation between these points. It is achieved by means of gradual rules which express that the closer the variable to the abscissa of a reference point, the closer the value of the function to the ordinate of this reference point. Gradual rules enable us to specify sophisticated gauges, under the form of connected areas, inside of which the function belonging to the class under consideration should remain. This provides a simple and efficient tool for categorizing signals. This tool can be further improved by making the gauge flexible by means of fuzzy gradual rules. This is illustrated on a benchmark example.

UAI Conference 2002 Conference Paper

Bipolar Possibilistic Representations

  • Salem Benferhat
  • Didier Dubois
  • Souhila Kaci
  • Henri Prade

Recently, it has been emphasized that the possibility theory framework allows us to distinguish between i) what is possible because it is not ruled out by the available knowledge, and ii) what is possible for sure. This distinction may be useful when representing knowledge, for modelling values which are not impossible because they are consistent with the available knowledge on the one hand, and values guaranteed to be possible because reported from observations on the other hand. It is also of interest when expressing preferences, to point out values which are positively desired among those which are not rejected. This distinction can be encoded by two types of constraints expressed in terms of necessity measures and in terms of guaranteed possibility functions, which induce a pair of possibility distributions at the semantic level. A consistency condition should ensure that what is claimed to be guaranteed as possible is indeed not impossible. The present paper investigates the representation of this bipolar view, including the case when it is stated by means of conditional measures, or by means of comparative context-dependent constraints. The interest of this bipolar framework, which has been recently stressed for expressing preferences, is also pointed out in the representation of diagnostic knowledge.

NMR Workshop 2002 Conference Paper

Possibility theory in information fusion

  • Didier Dubois
  • Henri Prade

Possibility theory and the body of aggregation operations from fuzzy set theory provide some tools to address the problem of merging information coming from several sources. Possibility theory is a representation framework that can model various kinds of information items: numbers, intervals, consonant random sets, special kind of probability families, as well as linguistic information, and —_ uncertain formulae in logical settings. The possibilistic approach to fusion is general enough to encompass logical modes of combination (conjunctive and disjunctive) as well as fusion modes used in statistics. This general framework allows to import inconsistency handling methods, inherited from logic, into numerical fusion problems. The approach applies to sensor fusion, aggregation of expert opinions as well as the merging of databases especially in case of poor, qualitative information.

UAI Conference 2001 Conference Paper

Graphical readings of possibilistic logic bases

  • Salem Benferhat
  • Didier Dubois
  • Souhila Kaci
  • Henri Prade

Possibility theory offers either a qualitive, or a numerical framework for representing uncertainty, in terms of dual measures of possibility and necessity. This leads to the existence of two kinds of possibilistic causal graphs where the conditioning is either based on the minimum , or the product operator. Benferhat et al. (1999) have investigated the connections between min-based graphs and possibilistic logic bases (made of classical formulas weighted in terms of certainty). This paper deals with a more difficult issue : the product-based graphical representations of possibilistic bases, which provides an easy structural reading of possibilistic bases.Moreover, this paper also provides another reading of possibilistic bases in terms of comparative preferences of the form "in the context p, q is preferred to not q". This enables us to explicit preferences underlying a set of goals with different levels of priority.

UAI Conference 2000 Conference Paper

A principled analysis of merging operations in possibilistic logic

  • Souhila Kaci
  • Salem Benferhat
  • Didier Dubois
  • Henri Prade

Possibilistic logic offers a qualitative framework for representing pieces of information associated with levels of uncertainty of priority. The fusion of multiple sources information is discussed in this setting. Different classes of merging operators are considered including conjunctive, disjunctive, reinforcement, adaptive and averaging operators. Then we propose to analyse these classes in terms of postulates. This is done by first extending the postulate for merging classical bases to the case where priorites are avaialbe.

AAAI Conference 1999 Conference Paper

A Sequential Reversible Belief Revision Method Based on Polynomials

  • Salem Benferhat
  • Didier Dubois
  • IRIT
  • Université Paul Sabatier; Odile Papini
  • LIM
  • Université de la Méditerranée

This paper deals with iterated belief changeand proposesa drastic revisionrule that modifiesa plausibility ordering of interpretations in such a waythat anyworld wherethe input observartion holds is moreplausible that any world where it does not. This change nile makessense in a dynamiccontext where observations are received, andthe newerobservations are considered more plausible than older ones. It is shownhowto encode an epistemic state using polynomials equipped with the lexicographical ordering. This encodingmakes it very easy to implement anditerate the revision rule using simple operations on these polynomials. Moreover, polynomials allowto keeptrack of the sequenceof observations. Lastly, it is shown howto efficiently compute the revision rule at the syntactical level, whenthe epistemicstate is conciselyrepresentedby a prioritized belief base. Ourrevision rule is the mostdrastic one can think of, in accordancewith Darwiche and Pearl’s principles, and thus contrasts with the minimalchange rule called natural belief revision.

UAI Conference 1999 Conference Paper

Assessing the value of a candidate: Comparing belief function and possibility theories

  • Didier Dubois
  • Michel Grabisch
  • Henri Prade
  • Philippe Smets

The problem of assessing the value of a candidate is viewed here as a multiple combination problem. On the one hand a candidate can be evaluated according to different criteria, and on the other hand several experts are supposed to assess the value of candidates according to each criterion. Criteria are not equally important, experts are not equally competent or reliable. Moreover levels of satisfaction of criteria, or levels of confidence are only assumed to take their values in qualitative scales which are just linearly ordered. The problem is discussed within two frameworks, the transferable belief model and the qualitative possibility theory. They respectively offer a quantitative and a qualitative setting for handling the problem, providing thus a way to compare the nature of the underlying assumptions.

UAI Conference 1999 Conference Paper

Possibilistic logic bases and possibilistic graphs

  • Salem Benferhat
  • Didier Dubois
  • Laurent Garcia
  • Henri Prade

Possibilistic logic bases and possibilistic graphs are two different frameworks of interest for representing knowledge. The former stratifies the pieces of knowledge (expressed by logical formulas) according to their level of certainty, while the latter exhibits relationships between variables. The two types of representations are semantically equivalent when they lead to the same possibility distribution (which rank-orders the possible interpretations). A possibility distribution can be decomposed using a chain rule which may be based on two different kinds of conditioning which exist in possibility theory (one based on product in a numerical setting, one based on minimum operation in a qualitative setting). These two types of conditioning induce two kinds of possibilistic graphs. In both cases, a translation of these graphs into possibilistic bases is provided. The converse translation from a possibilistic knowledge base into a min-based graph is also described.

UAI Conference 1998 Conference Paper

Comparative uncertainty, belief functions and accepted beliefs

  • Didier Dubois
  • Hélène Fargier
  • Henri Prade

This paper relates comparative belief structures and a general view of belief management in the setting of deductively closed logical representations of accepted beliefs. We show that the range of compatibility between the classical deductive closure and uncertain reasoning covers precisely the nonmonotonic 'preferential' inference system of Kraus, Lehmann and Magidor and nothing else. In terms of uncertain reasoning any possibility or necessity measure gives birth to a structure of accepted beliefs. The classes of probability functions and of Shafer's belief functions which yield belief sets prove to be very special ones.

AAAI Conference 1998 Conference Paper

Logical Representation and Computation of Optimal Decisions in a Qualitative Setting

  • Didier Dubois
  • Henri Prade

This paper describes a logical machineryfor computing decisions based on an ATMS procedure, wherethe available knowledge on the state of the world is describedbya possibilistic propositionallogic base(i. e. , a collectionof logical statements associatedwithqualitative certainty levels). Thepreferencesof the userare also describedbyanotherpossibilistic logic basewhose formulaweightsare interpreted in termsof priorities and formulasexpressgoals. Two attitudes are allowed for the decisionmaker: a pessimisticuncertainty-averse one and an optimistic one. Thecomputed decisions are in agreement witha qualitative counterpartto classical expectedutility theoryfor decisionunderuncertainty.

UAI Conference 1998 Conference Paper

Qualitative Decision Theory with Sugeno Integrals

  • Didier Dubois
  • Henri Prade
  • Régis Sabbadin

This paper presents an axiomatic framework for qualitative decision under uncertainty in a finite setting. The corresponding utility is expressed by a sup-min expression, called Sugeno (or fuzzy) integral. Technically speaking, Sugeno integral is a median, which is indeed a qualitative counterpart to the averaging operation underlying expected utility. The axiomatic justification of Sugeno integral-based utility is expressed in terms of preference between acts as in Savage decision theory. Pessimistic and optimistic qualitative utilities, based on necessity and possibility measures, previously introduced by two of the authors, can be retrieved in this setting by adding appropriate axioms.

UAI Conference 1997 Conference Paper

Decision-Making under Ordinal Preferences and Comparative Uncertainty

  • Didier Dubois
  • Hélène Fargier
  • Henri Prade

This paper investigates the problem of finding a preference relation on a set of acts from the knowledge of an ordering on events (subsets of states of the world) describing the decision-maker (DM)s uncertainty and an ordering of consequences of acts, describing the DMs preferences. However, contrary to classical approaches to decision theory, we try to do it without resorting to any numerical representation of utility nor uncertainty, and without even using any qualitative scale on which both uncertainty and preference could be mapped. It is shown that although many axioms of Savage theory can be preserved and despite the intuitive appeal of the method for constructing a preference over acts, the approach is inconsistent with a probabilistic representation of uncertainty, but leads to the kind of uncertainty theory encountered in non-monotonic reasoning (especially preferential and rational inference), closely related to possibility theory. Moreover the method turns out to be either very little decisive or to lead to very risky decisions, although its basic principles look sound. This paper raises the question of the very possibility of purely symbolic approaches to Savage-like decision-making under uncertainty and obtains preliminary negative results.

UAI Conference 1996 Conference Paper

Belief Revision with Uncertain Inputs in the Possibilistic Setting

  • Didier Dubois
  • Henri Prade

This paper discusses belief revision under uncertain inputs in the framework of possibility theory. Revision can be based on two possible definitions of the conditioning operation, one based on min operator which requires a purely ordinal scale only, and another based on product, for which a richer structure is needed, and which is a particular case of Dempster's rule of conditioning. Besides, revision under uncertain inputs can be understood in two different ways depending on whether the input is viewed, or not, as a constraint to enforce. Moreover, it is shown that M.A. Williams' transmutations, originally defined in the setting of Spohn's functions, can be captured in this framework, as well as Boutilier's natural revision.

UAI Conference 1996 Conference Paper

Coping with the Limitations of Rational Inference in the Framework of Possibility Theory

  • Salem Benferhat
  • Didier Dubois
  • Henri Prade

Possibility theory offers a framework where both Lehmann's "preferential inference" and the more productive (but less cautious) "rational closure inference" can be represented. However, there are situations where the second inference does not provide expected results either because it cannot produce them, or even provide counter-intuitive conclusions. This state of facts is not due to the principle of selecting a unique ordering of interpretations (which can be encoded by one possibility distribution), but rather to the absence of constraints expressing pieces of knowledge we have implicitly in mind. It is advocated in this paper that constraints induced by independence information can help finding the right ordering of interpretations. In particular, independence constraints can be systematically assumed with respect to formulas composed of literals which do not appear in the conditional knowledge base, or for default rules with respect to situations which are "normal" according to the other default rules in the base. The notion of independence which is used can be easily expressed in the qualitative setting of possibility theory. Moreover, when a counter-intuitive plausible conclusion of a set of defaults, is in its rational closure, but not in its preferential closure, it is always possible to repair the set of defaults so as to produce the desired conclusion.

UAI Conference 1995 Conference Paper

Numerical representations of acceptance

  • Didier Dubois
  • Henri Prade

Accepting a proposition means that our confidence in this proposition is strictly greater than the confidence in its negation. This paper investigates the subclass of uncertainty measures, expressing confidence, that capture the idea of acceptance, what we call acceptance functions. Due to the monotonicity property of confidence measures, the acceptance of a proposition entails the acceptance of any of its logical consequences. In agreement with the idea that a belief set (in the sense of Gardenfors) must be closed under logical consequence, it is also required that the separate acceptance o two propositions entail the acceptance of their conjunction. Necessity (and possibility) measures agree with this view of acceptance while probability and belief functions generally do not. General properties of acceptance functions are estabilished. The motivation behind this work is the investigation of a setting for belief revision more general than the one proposed by Alchourron, Gardenfors and Makinson, in connection with the notion of conditioning.

UAI Conference 1995 Conference Paper

Practical model-based diagnosis with qualitative possibilistic uncertainty

  • Didier Cayrac
  • Didier Dubois
  • Henri Prade

An approach to fault isolation that exploits vastly incomplete models is presented. It relies on separate descriptions of each component behavior, together with the links between them, which enables focusing of the reasoning to the relevant part of the system. As normal observations do not need explanation, the behavior of the components is limited to anomaly propagation. Diagnostic solutions are disorders (fault modes or abnormal signatures) that are consistent with the observations, as well as abductive explanations. An ordinal representation of uncertainty based on possibility theory provides a simple exception-tolerant description of the component behaviors. We can for instance distinguish between effects that are more or less certainly present (or absent) and effects that are more or less certainly present (or absent) when a given anomaly is present. A realistic example illustrates the benefits of this approach.

UAI Conference 1994 Conference Paper

An Ordinal View of Independence with Application to Plausible Reasoning

  • Didier Dubois
  • Luis Fariñas del Cerro
  • Andreas Herzig
  • Henri Prade

An ordinal view of independence is studied in the framework of possibility theory. We investigate three possible definitions of dependence, of increasing strength. One of them is the counterpart to the multiplication law in probability theory, and the two others are based on the notion of conditional possibility. These two have enough expressive power to support the whole possibility theory, and a complete axiomatization is provided for the strongest one. Moreover we show that weak independence is well-suited to the problems of belief change and plausible reasoning, especially to address the problem of blocking of property inheritance in exception-tolerant taxonomic reasoning.

AAAI Conference 1994 Conference Paper

Can We Enforce Full Compositionality in Uncertainty Calculi?

  • Didier Dubois

At AAAI'93, Elkan has claimed to have a result trivializing fuzzy logic. This trivialization is based on too strong a view of equivalence in fuzzy logic and relates to a fully compositional treatment of uncertainty. Such a treatment is shown to be impossible in this paper. We emphasize the distinction between i) degrees of partial truth which are allowed to be truth functional and which pertain to gradual (or fuzzy) propositions, and ii) degrees of uncertainty which cannot be compositional with respect to all the connectives when attached to classical propositions. This distinction is exemplified by the difference between fuzzy logic and possibilistic logic. We also investigate an almost compositional uncertainty calculus, but it is shown to lack expressiveness.

UAI Conference 1993 Conference Paper

A fuzzy relation-based extension of Reggia's relational model for diagnosis handling uncertain and incomplete information

  • Didier Dubois
  • Henri Prade

Relational models for diagnosis are based on a direct description of the association between disorders and manifestations. This type of model has been specially used and developed by Reggia and his co-workers in the late eighties as a basic starting point for approaching diagnosis problems. The paper proposes a new relational model which includes Reggia's model as a particular case and which allows for a more expressive representation of the observations and of the manifestations associated with disorders. The model distinguishes, i) between manifestations which are certainly absent and those which are not (yet) observed, and ii) between manifestations which cannot be caused by a given disorder and manifestations for which we do not know if they can or cannot be caused by this disorder. This new model, which can handle uncertainty in a non-probabilistic way, is based on possibility theory and so-called twofold fuzzy sets, previously introduced by the authors.

UAI Conference 1993 Conference Paper

Argumentative inference in uncertain and inconsistent knowledge bases

  • Salem Benferhat
  • Didier Dubois
  • Henri Prade

This paper presents and discusses several methods for reasoning from inconsistent knowledge bases. A so-called argumentative-consequence relation taking into account the existence of consistent arguments in favor of a conclusion and the absence of consistent arguments in favor of its contrary, is particularly investigated. Flat knowledge bases, i.e. without any priority between their elements, as well as prioritized ones where some elements are considered as more strongly entrenched than others are studied under different consequence relations. Lastly a paraconsistent-like treatment of prioritized knowledge bases is proposed, where both the level of entrenchment and the level of paraconsistency attached to a formula are propagated. The priority levels are handled in the framework of possibility theory.

IJCAI Conference 1993 Conference Paper

Belief revision and updates in numerical formalisms� An overview, with new results for the possibilistic framework

  • Didier Dubois
  • Henri Prade

The difference between Bayesian conditioning and Lewis' imaging is somewhat similar to the one between Gardenfors' belief revision and Katsuno and Mendelzon' updating in the logical framework. Counterparts in possibility theory of these two operations are presented, including the case of conditioning upon an uncertain observation. Possibilistic conditioning satisfies all the postulates for belief revision, and possibilistic imaging all the updating postulates. Lastly, a third operation called "focusing" is naturally introduced in the setting of belief and plausibility functions.

IJCAI Conference 1993 Conference Paper

Inconsistency management and prioritized syntax-based entailment

  • Salem Benferhat
  • Claudette Cayrol
  • Didier Dubois
  • Jerome Lang
  • Henri Prade

The idea of ordering plays a basic role in commonsense reasoning for addressing three interrelated tasks: inconsistency handling, belief revision and plausible inference. We study the behavior of non-monotonic inferences induced by various methods for priority-based handling of inconsistent sets of classical formulas. One of them is based on a lexicographic ordering of maximal consistent subsets, and refines Brewka's preferred sub-theories. This new approach leads to a non-monotonic inference which satisfies the "rationality" property while solving the problem of blocking of property inheritance. It differs from and improves previous equivalent approaches such as Gardenfors and Makinson's expectation-based inference, Pearl's System Z and possibilistic logic.

UAI Conference 1992 Conference Paper

A Symbolic Approach to Reasoning with Linguistic Quantifiers

  • Didier Dubois
  • Henri Prade
  • Lluís Godo
  • Ramón López de Mántaras

This paper investigates the possibility of performing automated reasoning in probabilistic logic when probabilities are expressed by means of linguistic quantifiers. Each linguistic term is expressed as a prescribed interval of proportions. Then instead of propagating numbers, qualitative terms are propagated in accordance with the numerical interpretation of these terms. The quantified syllogism, modelling the chaining of probabilistic rules, is studied in this context. It is shown that a qualitative counterpart of this syllogism makes sense, and is relatively independent of the threshold defining the linguistically meaningful intervals, provided that these threshold values remain in accordance with the intuition. The inference power is less than that of a full-fledged probabilistic con-quaint propagation device but better corresponds to what could be thought of as commonsense probabilistic reasoning.

UAI Conference 1991 Conference Paper

A Logic of Graded Possibility and Certainty Coping with Partial Inconsistency

  • Jérôme Lang
  • Didier Dubois
  • Henri Prade

A semantics is given to possibilistic logic, a logic that handles weighted classical logic formulae, and where weights are interpreted as lower bounds on degrees of certainty or possibility, in the sense of Zadeh's possibility theory. The proposed semantics is based on fuzzy sets of interpretations. It is tolerant to partial inconsistency. Satisfiability is extended from interpretations to fuzzy sets of interpretations, each fuzzy set representing a possibility distribution describing what is known about the state of the world. A possibilistic knowledge base is then viewed as a set of possibility distributions that satisfy it. The refutation method of automated deduction in possibilistic logic, based on previously introduced generalized resolution principle is proved to be sound and complete with respect to the proposed semantics, including the case of partial inconsistency.

UAI Conference 1991 Conference Paper

Constraint Propagation with Imprecise Conditional Probabilities

  • Stéphane Amarger
  • Didier Dubois
  • Henri Prade

An approach to reasoning with default rules where the proportion of exceptions, or more generally the probability of encountering an exception, can be at least roughly assessed is presented. It is based on local uncertainty propagation rules which provide the best bracketing of a conditional probability of interest from the knowledge of the bracketing of some other conditional probabilities. A procedure that uses two such propagation rules repeatedly is proposed in order to estimate any simple conditional probability of interest from the available knowledge. The iterative procedure, that does not require independence assumptions, looks promising with respect to the linear programming method. Improved bounds for conditional probabilities are given when independence assumptions hold.

IJCAI Conference 1991 Conference Paper

Possibilistic Logic, Preferential Models, Non-monotonic-ity and Related Issues

  • Didier Dubois
  • Henri

The links between Shoham's preference logic and possibilistic logic, a numerical logic of uncertainty based on Zadeh's possibility measures, are investigated. Starting from a fuzzy set of preferential interpretations of a propositional theory, we prove that the notion of preferential entailment is closely related to a previously introduced notion of conditional possibility. Conditional possibility is then shown to possess all properties (originally stated by Gabbay) of a well-behaved non-monotonic consequence relation. We obtain the possibilistic counterpart of Adams' e-semantics of conditional probabilities which is the basis of the probabilistic model of non-monotonic logic proposed by Geffner and Pearl. Lastly we prove that our notion of possibilistic entailment is the one at work in possibilistic logic, a logic that handles uncertain propositional formulas, where uncertainty is modelled by degrees of necessity, and where partial inconsistency is allowed. Considering the formerly established close links between Gardenfors'epistemic entrenchment and necessity measures, what this paper proposes is a new way of relating belief revision and non-monotonic inference, namely via possibility theory.

UAI Conference 1990 Conference Paper

Updating with belief functions, ordinal conditional functions and possibility measures

  • Didier Dubois
  • Henri Prade

This paper discusses how a measure of uncertainty representing a state of knowledge can be updated when a new information, which may be pervaded with uncertainty, becomes available. This problem is considered in various framework, namely: Shafer's evidence theory, Zadeh's possibility theory, Spohn's theory of epistemic states. In the two first cases, analogues of Jeffrey's rule of conditioning are introduced and discussed. The relations between Spohn's model and possibility theory are emphasized and Spohn's updating rule is contrasted with the Jeffrey-like rule of conditioning in possibility theory. Recent results by Shenoy on the combination of ordinal conditional functions are reinterpreted in the language of possibility theory. It is shown that Shenoy's combination rule has a well-known possibilistic counterpart.

IJCAI Conference 1989 Conference Paper

Measure-Free Conditioning, Probability and Non-Monotonic Reasoning

  • Didier Dubois
  • Henri Pradc

Recent results in the foundations of probability theory indicate that a conditional probability can be viewed as a probability attached to a mathematical entity called a measure-free conditional. Such a measure-free conditional can receive a semantics in terms of a trivalent logic and logical operations are defined on conditionals in terms of truth-tables. It is shown that these results can be useful to justify Cox's axiomatic framework for probability as well as its application to other theories of uncertainty (Shafer's plausibility functions and Zadeh's possibility measures). Moreover it is shown that measure-free conditionals have the properties of well-behaved non-monotonic inference rules.

ICRA Conference 1986 Conference Paper

An expert-system approach to industrial job-shop scheduling

  • Eric Bensana
  • M. Correge
  • Gérard Bel
  • Didier Dubois

The first part of this paper is devoted to the presentation of a software system for jobshop scheduling of batches under due date constraints and time-table constraints on work-stations. It is based on a heuristic technique for sequencing the jobs. It is actually run in a real workshop. The second part of the parper deals with an expert system methodology currently under development for the same kind of problems. The idea is to be able to integrate two kinds of knowledge about the production process: theoretical knowledge (issued from scheduling theory) which achieves the management of time, and practical knowledge (provided by the shop-floor manager) about specific technological constraints which must be satisfied by the actual production process. These constraints are usually not taken into account at the theoretical level. The knowledge representation and processing techniques of Artificial Intelligence enable realistic schedules to be obtained by simultaneous use of both sources of information.