A Default Logic Patch for Default Logic

International audienceThis paper is about the fusion of multiple information sources represented using default logic. More precisely, the focus is on solving the problem that occurs when the standard-logic knowledge parts of the sources are contradictory, as default theories trivialize in this case. To overcome this problem, it is shown that replacing each formula belonging to Minimally Unsatisfiable Subformulas by a corresponding supernormal default allows appealing features. Moreover, it is investigated how these additional defaults interact with the initial defaults of the theory. Interestingly, this approach allows us to handle the problem of default theories containing inconsistent standard-logic knowledge, using the default logic framework itself

On Finding Minimally Unsatisfiable Cores of CSPs

International audienceWhen a Constraint Satisfaction Problem (CSP) admits no solution, it can be useful to pinpoint which constraints are actually contradicting one another and make the problem infeasible. In this paper, a recent heuristic-based approach to compute infeasible min- imal subparts of discrete CSPs, also called Minimally Unsatisfiable Cores (MUCs), is improved. The approach is based on the heuristic exploitation of the number of times each constraint has been falsified during previous failed search steps. It appears to en- hance the performance of the initial technique, which was the most efficient one until now

Representative explanations for over-constrained problems

In many interactive decision making scenarios there is often no solution that satisfies all of the user's preferences. The decision process can be helped by providing explanations. Relaxations show sets of consistent preferences and, thus, indicate which preferences can be enforced, while exclusion sets show which preferences can be relaxed to obtain a solution. We propose a new approach to explanation based on the notion of a representative set of explanations. The size of the set of explanations we compute is exponentially more compact than that found using common approaches from the literature based on finding all minimal conflicts. Copyright © 2007, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved

Preuves de non réalisabilité et filtrage de domaines pour les problèmes de satisfaction de contraintes : application à la confection d'horaires

Contexte global -- Objectifs de cette thèse -- Organisation de la thèse -- Notions préliminaires -- Problèmes de coloration de graphes -- Les problèmes de sastisfaction de contraintes -- Problème SAT de satisfaisabilité booléenne -- Programmation par contraintes -- Sous-ensembles incohérents irréductibles -- Revue de la littérature concernant l'extraction d'IIS dans les CSP, la résolution du problème SAT et l'extraction d'IIS pour le problème SAT -- Détection de sous-ensembles incohérents dans des CSP -- Le problème SAT et sa résolution -- Utilisation d'heuristiques pour trouver des sous-ensembles incohérents minimaux pour le problème SAT -- Algorithmes de détection d'IIS -- Autres procédures -- Algorithme tabou pour Max WSAT -- Détails d'implémentation -- Résultats expérimentaux -- Revue de la littérature concernant le filtrage de contraintes globales de CSP -- Algorithme de filtrage pour la contrainte AllDifferent -- Algorithme de filtrage de domaines pour la contrainte SomeDifferent -- Autres travaux concernant le filtrage de contraintes globales -- Algorithme de filtrage pour la contrainte SomeDifferent -- Description de l'algorithme de filtrage -- Résultats expérimentaux -- Revue de la littérature concernant le problème de confection d'horaires pour le personnel navigant aérien -- Les méthodes de résolution du PBS -- Détection de sous-ensembles incohérents minimaux dans le problème de confection d'horaires pour le personnel navigant aérien -- Algorithmes de détection de sous-ensembles incohérents minimaux -- Algorithme tabou -- Algorithme exact de vérification des sous-problèmes incohérents -- Résultats expérimentaux -- Méthodes de recherche locale

Proceedings of the 18th Irish Conference on Artificial Intelligence and Cognitive Science

These proceedings contain the papers that were accepted for publication at AICS-2007, the 18th Annual Conference on Artificial Intelligence and Cognitive Science, which was held in the Technological University Dublin; Dublin, Ireland; on the 29th to the 31st August 2007. AICS is the annual conference of the Artificial Intelligence Association of Ireland (AIAI)

A constraint solver for software engineering : finding models and cores of large relational specifications

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 105-120).Relational logic is an attractive candidate for a software description language, because both the design and implementation of software often involve reasoning about relational structures: organizational hierarchies in the problem domain, architectural configurations in the high level design, or graphs and linked lists in low level code. Until recently, however, frameworks for solving relational constraints have had limited applicability. Designed to analyze small, hand-crafted models of software systems, current frameworks perform poorly on specifications that are large or that have partially known solutions. This thesis presents an efficient constraint solver for relational logic, with recent applications to design analysis, code checking, test-case generation, and declarative configuration. The solver provides analyses for both satisfiable and unsatisfiable specifications--a finite model finder for the former and a minimal unsatisfiable core extractor for the latter. It works by translating a relational problem to a boolean satisfiability problem; applying an off-the-shelf SAT solver to the resulting formula; and converting the SAT solver's output back to the relational domain. The idea of solving relational problems by reduction to SAT is not new. The core contributions of this work, instead, are new techniques for expanding the capacity and applicability of SAT-based engines. They include: a new interface to SAT that extends relational logic with a mechanism for specifying partial solutions; a new translation algorithm based on sparse matrices and auto-compacting circuits; a new symmetry detection technique that works in the presence of partial solutions; and a new core extraction algorithm that recycles inferences made at the boolean level to speed up core minimization at the specification level.by Emina Torlak.Ph.D