Expressive Power of Weighted Propositional Formulas for Cardinal Preference Modelling

As proposed in various places, a set of propositional formulas, each associated with a numerical weight, can be used to model the preferences of an agent in combinatorial domains. If the range of possible choices can be represented by the set of possible assignments of propositional symbols to truth values, then the utility of an assignment is given by the sum of the weights of the formulas it satisfies. Our aim in this paper is twofold: (1) to establish correspondences between certain types of weighted formulas and well-known classes of utility functions (such as monotonic, concave or k-additive functions); and (2) to obtain results on the comparative succinctness of different types of weighted formulas for representing the same class of utility functions

From preference logics to preference languages, and back

Preference logics and AI preference representation languages are both concerned with reasoning about preferences on combinatorial domains, yet so far these two streams of research have had little interaction. This paper contributes to the bridging of these areas. We start by constructing a "prototypical" preference logic, which combines features of existing preference logics, and then we show that many well-known preference languages, such as CP-nets and its extensions, are natural fragments of it. After establishing useful characterizations of dominance and consistency in our logic, we study the complexity of satisfiability in the general case as well as for meaningful fragments, and we study the expressive power as well as the relative succinctness of some of these fragments

Relation-changing modal operators

We study dynamic modal operators that can change the accessibility relation of a model during the evaluation of a formula. In particular, we extend the basic modal language with modalities that are able to delete, add or swap an edge between pairs of elements of the domain. We define a generic framework to characterize this kind of operations. First, we investigate relation-changing modal logics as fragments of classical logics. Then, we use the new framework to get a suitable notion of bisimulation for the logics introduced, and we investigate their expressive power. Finally, we show that the complexity of the model checking problem for the particular operators introduced is PSpace-complete, and we study two subproblems of model checking: formula complexity and program complexity.Fil: Areces, Carlos Eduardo. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Fervari, Raul Alberto. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Hoffmann, Guillaume Emmanuel. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Compact preference representation and combinatorial vote

In many real-world social choice problems, the set of alternatives is defined as the Cartesian product of (finite) domain values for each of a given set of variables, and these variables cannot be asusmed to be prefentially independent (to take an example, if X is the main dish of a dinner and Y the wine, preferences over Y depends on the value taken for X). Such combinatorial domains are much too large to allow for representing preference relations or utility functions explicitly (that is, by listing alternatives together with their rank or utility); for this reason, artificial intelligence researchers have been developing languages for specifying preference relations or utility functions as compactly as possible. This paper first gives a brief survey of compact representation languages, and then discusses its role for representing and solving social choice problems, especially from the point of view of computational complexity

Logics of Imprecise Comparative Probability

This paper studies connections between two alternatives to the standard probability calculus for representing and reasoning about uncertainty: imprecise probability andcomparative probability. The goal is to identify complete logics for reasoning about uncertainty in a comparative probabilistic language whose semantics is given in terms of imprecise probability. Comparative probability operators are interpreted as quantifying over a set of probability measures. Modal and dynamic operators are added for reasoning about epistemic possibility and updating sets of probability measures

A Logic for Collective Choice

International audienceThis paper presents a modal logic for modelling individual and collective choices over a set of feasible alternatives. The logic extends propositional logic with a binary modality so that a formula can express not only properties of alternatives but also priorities of individuals over the properties. More importantly, each formula of this logic determines a preference ordering over alternatives based on the priorities over properties that the formula expresses. In such a way, preferences of multiple agents can be represented by a set of formulas in the same logic. This allows us to treat the problem of collective choice in a multi-agent system as aggregation of logical formulas. We further use this language to express a few plausible collective choice rules. Similar to preference aggregation, we specify collective choice rules by Arrow’s conditions. Interestingly, all Arrowian conditions are plausible under the new setting except Independence of Irrelevant Alternatives. This gives us a natural way to avoid Arrow’s impossibility result. Finally, we develop a model checking algorithm to automatically generate individual and collective choices in the logic

Complexity of Nested Circumscription and Nested Abnormality Theories

The need for a circumscriptive formalism that allows for simple yet elegant modular problem representation has led Lifschitz (AIJ, 1995) to introduce nested abnormality theories (NATs) as a tool for modular knowledge representation, tailored for applying circumscription to minimize exceptional circumstances. Abstracting from this particular objective, we propose L_{CIRC}, which is an extension of generic propositional circumscription by allowing propositional combinations and nesting of circumscriptive theories. As shown, NATs are naturally embedded into this language, and are in fact of equal expressive capability. We then analyze the complexity of L_{CIRC} and NATs, and in particular the effect of nesting. The latter is found to be a source of complexity, which climbs the Polynomial Hierarchy as the nesting depth increases and reaches PSPACE-completeness in the general case. We also identify meaningful syntactic fragments of NATs which have lower complexity. In particular, we show that the generalization of Horn circumscription in the NAT framework remains CONP-complete, and that Horn NATs without fixed letters can be efficiently transformed into an equivalent Horn CNF, which implies polynomial solvability of principal reasoning tasks. Finally, we also study extensions of NATs and briefly address the complexity in the first-order case. Our results give insight into the ``cost'' of using L_{CIRC} (resp. NATs) as a host language for expressing other formalisms such as action theories, narratives, or spatial theories.Comment: A preliminary abstract of this paper appeared in Proc. Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-01), pages 169--174. Morgan Kaufmann, 200

Logics for strategic reasoning and collective decision-making

Cette thèse aborde le problème du raisonnement stratégique. Le raisonnement stratégique est un thème de recherches existant depuis e nombreuses années en théorie des jeux. Toutefois, celui-ci a le plus souvent pour objet de déterminer si des équilibres stratégiques existent sans détailler la définition en elle-même de ces stratégies. La construction d'agents artificiels capable de raisonner stratégiquement implique de se poser la question de la représentation de ces stratégies afin que les agents puissent les construire, combiner, comparer et enfin et surtout exécuter. Cette thèse propose un ensemble de logiques pour le raisonnement stratégique et la prise de décision collective. Elle établit dans un premier temps un cadre unifiée pour la définition de jeux, la représentation de stratégies et le raisonnement sur celles-ci dans le contexte des jeux à information parfaite. Ce cadre est ensuite étendu pour prendre en compte les jeux à information imparfaite. Les relations entre les connaissances de groupe, le pouvoir des coalitions ainsi que le partage d'informations dans une coalition sont ensuite étudiés. Dans un dernier temps, est introduit une logique modale permettant de de raisonner sur les choix collectifs, cette logique permet de généraliser les approches logiques existantes pour l'agrégation de jugements. La complexité de ces différents cadres logiques est aussi étudiée et nous montrons que ces différents cadres offre un équilibre pertinent entre efficacité computationnelle et pouvoir d'expression.This thesis proposes a set of logics for modelling strategic reasoning and collective decision-making. It first establishes a unified logical framework for game specifications, strategy representation and strategic reasoning in perfect information games. Based on that, it proposes an epistemic extension to address imperfect information games. To investigate the interplay of group knowledge and coalitional abilities, it further models knowledge sharing within coalitions. Finally it introduces a modal logic for collective choice and generalizes the logic-based approach to judgment aggregation. The complexity analysis of these logics indicates that these frameworks make a good balance between expressive power and computational efficiency