Algorithms for enriched abstract argumentation frameworks for large-scale cases

La théorie de l'argumentation abstraite propose des méthodes pour représenter et traiter les informations potentiellement incohérentes, et pour en tirer des conclusions ou prendre des décisions. Une telle approche est dite abstraite car elle se concentre uniquement sur la manière dont les arguments s'influencent mutuellement et pas sur la constitution des arguments. Les arguments sont donc considérés comme des entités génériques qui interagissent positivement (relation de support) ou négativement (relation d'attaque) les unes avec les autres. Ce niveau d'abstraction permet de proposer des processus de raisonnement génériques qui peuvent être appliqués à toute définition ou formalisme concret des arguments. Le modèle de raisonnement basé sur l'argumentation est appliqué dans les systèmes multi-agents depuis des années. Le développement des techniques d'argumentation et de leur calcul est un point clé de ces applications. C'est la motivation même de mon travail : améliorer l'utilisation de l'argumentation abstraite en développant de meilleurs outils pour sa mise en oeuvre. De nombreux cadres d'argumentation et sémantiques associées ont été proposés dans la littérature pour améliorer l'expressivité de l'argumentation abstraite. Alors qu'un cadre donné spécifie la manière de représenter et d'exprimer un problème d'argumentation (types de relations entre les arguments, poids des attaques ou des arguments, relation d'ordre supérieur, etc.), une sémantique, pour un cadre d'argumentation spécifique, capture ce qui est une solution d'un problème d'argumentation, dans le sens de ce qui est acceptable. Dans mon travail, je me suis d'abord concentré sur la résolution efficace de certains problèmes d'argumentation qui sont exprimés dans le cadre d'argumentation classique et les sémantiques définis par Dung. Les sémantiques de Dung produisent des ensembles d'arguments conjointement acceptables, appelés extensions. Mon travail a conduit à la proposition d'un nouvel algorithme distribué et basé sur une technique de clustering pour calculer les extensions sous les sémantiques de Dung. Il a été conçu pour certains types de cadres d'argumentation de "grande échelle", produisant un grand nombre d'extensions. Il a été implémenté et testé. Les résultats des tests montrent toute son efficacité pour les cadres d'argumentation à grande échelle ciblés. Je me suis ensuite intéressé aux cadres d'argumentation d'ordre supérieur, et en particulier au cadre d'argumentation récursif (RAF). Dans ce contexte, une attaque peut avoir comme cible une autre attaque : un argument peut ainsi être acceptable alors même qu'il est attaqué parce que cette attaque (recevant elle-même une attaque) peut être invalide, et donc non pertinente contre sa cible. Là où le cadre de Dung produit des extensions, les sémantiques des RAF produisent des "structures", des paires dont le premier élément est un ensemble d'arguments et le second un ensemble d'attaques. Si des algorithmes existaient déjà pour le cadre de Dung, il n'en était pas de même pour les RAF. J'ai donc commencé par étudier la complexité des sémantiques des RAF. J'ai ensuite étendu la notion de labelling aux RAF, une autre caractérisation de l'acceptabilité déjà existante dans le cadre de Dung. La notion de "composante fortement connexe" a été élargie aux RAF, et les propriétés de décomposabilité des sémantiques des RAF ont été étudiées. Toutes ces contributions ouvrent la voie à de futurs algorithmes pour calculer l'acceptabilité sous plusieurs sémantiques des RAF.Abstract argumentation theory proposes methods to represent and deal with contentious information, and to draw conclusions or take decision from it. Such an abstract approach focuses on how arguments affect each other. Arguments are seen as generic entities which interact positively (support relation) or negatively (attack relation) with each other. This abstraction level allows to propose generic reasoning processes that can be applied to any concrete definition or formalism for arguments. Argumentation-based reasoning model has been of application in multi-agent systems for years now. The development of argumentation techniques and of their computation drives such applications. This is the very motivation of this thesis: enhancing the use of abstract argumentation by developing better tools for its application. A lot of frameworks and semantics have been proposed to enhance expressivity in abstract argumentation. While a given framework specifies the way of representing and expressing an argumentation problem (types of relations between arguments, weight on attacks or arguments, higher-order relation, etc.), a semantics, defined for a specific argumentation framework, captures what is a solution of an argumentation problem, in the sense of what is acceptable. In this thesis, I first focus on solving more efficiently argumentation problems which are expressed in the basic, seminal argumentation framework and semantics defined by Dung. Dung's semantics produce sets of jointly acceptable arguments, called extensions. A new distributed and clustering based algorithm to compute Dung's semantics is my first contribution. This algorithm has been designed for certain types of large-scale argumentation frameworks, that produce a large number of extensions. It has been implemented and tested. The results of these tests show its efficiency in the context of the large scale argumentation frameworks which are targeted. Second, I focus on argumentation frameworks with higher order attacks, and especially Recursive Argumentation Frameworks (RAF). In this context, an attack may have as target an attack: an argument may thus be acceptable while one of its attack (receiving itself an attack) may be invalid, and so non pertinent against its target. Similarly to Dung's semantics which produce extensions, the RAF semantics produce "structures", pairs whose first element is a set of arguments and the second a set of attacks. If algorithms already existed for Dung's framework, it was not the case for RAF. In order to address this issue, I start with studying the complexity of RAF semantics. I then extend the notion of labelling to RAF, another kind of characterization of acceptability which already existed for Dung's framework. The notion of "strongly connected component" is extended to RAF and decomposability properties of RAF semantics are studied. All these contributions pave the way for future algorithms to compute acceptability under RAF semantics

Proceedings of the 11th European Agent Systems Summer School Student Session

This volume contains the papers presented at the Student Session of the 11th European Agent Systems Summer School (EASSS) held on 2nd of September 2009 at Educatorio della Providenza, Turin, Italy. The Student Session, organised by students, is designed to encourage student interaction and feedback from the tutors. By providing the students with a conference-like setup, both in the presentation and in the review process, students have the opportunity to prepare their own submission, go through the selection process and present their work to each other and their interests to their fellow students as well as internationally leading experts in the agent field, both from the theoretical and the practical sector. Table of Contents: Andrew Koster, Jordi Sabater Mir and Marco Schorlemmer, Towards an inductive algorithm for learning trust alignment . . . 5; Angel Rolando Medellin, Katie Atkinson and Peter McBurney, A Preliminary Proposal for Model Checking Command Dialogues. . . 12; Declan Mungovan, Enda Howley and Jim Duggan, Norm Convergence in Populations of Dynamically Interacting Agents . . . 19; Akın Günay, Argumentation on Bayesian Networks for Distributed Decision Making . . 25; Michael Burkhardt, Marco Luetzenberger and Nils Masuch, Towards Toolipse 2: Tool Support for the JIAC V Agent Framework . . . 30; Joseph El Gemayel, The Tenacity of Social Actors . . . 33; Cristian Gratie, The Impact of Routing on Traffic Congestion . . . 36; Andrei-Horia Mogos and Monica Cristina Voinescu, A Rule-Based Psychologist Agent for Improving the Performances of a Sportsman . . . 39; --Autonomer Agent,Agent,Künstliche Intelligenz

Online Handbook of Argumentation for AI: Volume 1

This volume contains revised versions of the papers selected for the first volume of the Online Handbook of Argumentation for AI (OHAAI). Previously, formal theories of argument and argument interaction have been proposed and studied, and this has led to the more recent study of computational models of argument. Argumentation, as a field within artificial intelligence (AI), is highly relevant for researchers interested in symbolic representations of knowledge and defeasible reasoning. The purpose of this handbook is to provide an open access and curated anthology for the argumentation research community. OHAAI is designed to serve as a research hub to keep track of the latest and upcoming PhD-driven research on the theory and application of argumentation in all areas related to AI.Comment: editor: Federico Castagna and Francesca Mosca and Jack Mumford and Stefan Sarkadi and Andreas Xydi

Reweighted belief propagation and quiet planting for random K-SAT

We study the random K-satisfiability problem using a partition function where each solution is reweighted according to the number of variables that satisfy every clause. We apply belief propagation and the related cavity method to the reweighted partition function. This allows us to obtain several new results on the properties of random K-satisfiability problem. In particular the reweighting allows to introduce a planted ensemble that generates instances that are, in some region of parameters, equivalent to random instances. We are hence able to generate at the same time a typical random SAT instance and one of its solutions. We study the relation between clustering and belief propagation fixed points and we give a direct evidence for the existence of purely entropic (rather than energetic) barriers between clusters in some region of parameters in the random K-satisfiability problem. We exhibit, in some large planted instances, solutions with a non-trivial whitening core; such solutions were known to exist but were so far never found on very large instances. Finally, we discuss algorithmic hardness of such planted instances and we determine a region of parameters in which planting leads to satisfiable benchmarks that, up to our knowledge, are the hardest known.Comment: 23 pages, 4 figures, revised for readability, stability expression correcte

A Discussion Game for the Credulous Decision Problem of Abstract Dialectical Frameworks under Preferred Semantics

Abstract dialectical frameworks (ADFs) have been introduced as a general formalism for modeling and evaluating argumentation. However, the role of discussion in reasoning in ADFs has not been clarified well so far. The current work presents a discussion game, as a proof method, to answer credulous decision problems of ADFs under preferred semantics. The game can be the basis for an algorithm that can be used not only for answering the decision problem but also for human-machine interaction