A formal characterization of the outcomes of rule-based argumentation systems (SUM 2013)

International audienceRule-based argumentation systems are developed for reasoning about defeasible information. As a major feature, their logical language distinguishes between strict rules and defeasible ones. This paper presents the first study on the outcomes of such systems under various semantics such as naive, stable, preferred, ideal and grounded. For each of these semantics, it characterizes both the extensions and the set of plausible inferences drawn by these systems under a few intuitive postulates

Logical limits of abstract argumentation frameworks

International audienceDung’s (1995) argumentation framework takes as input two abstract entities: a set of arguments and a binary relation encoding attacks between these arguments. It returns acceptable sets of arguments, called extensions, w.r.t. a given semantics. While the abstract nature of this setting is seen as a great advantage, it induces a big gap with the application that it is used to. This raises some questions about the compatibility of the setting with a logical formalism (i.e., whether it is possible to instantiate it properly from a logical knowledge base), and about the significance of the various semantics in the application context. In this paper we tackle the above questions. We first propose to fill in the previous gap by extending Dung’s (1995) framework. The idea is to consider all the ingredients involved in an argumentation process. We start with the notion of an abstract monotonic logic which consists of a language (defining the formulas) and a consequence operator. We show how to build, in a systematic way, arguments from a knowledge base formalised in such a logic. We then recall some basic postulates that any instantiation should satisfy. We study how to choose an attack relation so that the instantiation satisfies the postulates. We show that symmetric attack relations are generally not suitable. However, we identify at least one ‘appropriate’ attack relation. Next, we investigate under stable, semi-stable, preferred, grounded and ideal semantics the outputs of logic-based instantiations that satisfy the postulates. For each semantics, we delimit the number of extensions an argumentation system may have, characterise the extensions in terms of subsets of the knowledge base, and finally characterise the set of conclusions that are drawn from the knowledge base. The study reveals that stable, semi-stable and preferred semantics either lead to counter-intuitive results or provide no added value w.r.t. naive semantics. Besides, naive semantics either leads to arbitrary results or generalises the coherence-based approach initially developed by Rescher and Manor (1970). Ideal and grounded semantics either coincide and generalise the free consequence relation developed by Benferhat, Dubois, and Prade (1997), or return arbitrary results. Consequently, Dung’s (1995) framework seems problematic when applied over deductive logical formalisms

Practical reasoning as a generalized decision making problem

LAMSADE : Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décisionInternational audienceDecision making, often viewed as a form of reasoning toward action, has been considered from different points of view. Classical decision theory, as developed by economists, has focused mainly on identifying criteria such as expected utility for comparing different alternatives. The inputs of this approach are a set of feasible atomic actions, and a function that assesses the value of their consequences when the actions are performed in a given state. One of the main practical limitation of this approach is the fact that it reduces the whole decision problem to the availability of two functions: a probability distribution and a utility function. This is why some researchers in AI have advocated the need for a different approach in which all the aspects that may be involved in a decision problem (such as the desires of an agent, the feasibility of actions, etc) are explicitly represented. Hence, BDI architectures have been developed. They take their inspiration in the work of philosophers who have advocated practical reasoning. Practical reasoning mainly deals with the adoption, filling in, and reconsideration of intentions. However, these approaches suffer from a lack of a clear formulation of decision rules that combine the above qualitative concepts to decide which action to perform. In this paper, we argue that practical reasoning is a generalized decision making problem. The basic idea is that instead of comparing atomic actions, one has to compare sets of actions. The preferred set of actions becomes the intentions of the agent. The paper presents a unified setting that benefits from the advantages of the three above-mentioned approaches (classical decision, 15 BDI, practical reasoning). More precisely, we propose a formal framework that takes as input a set of beliefs, a set of conditional desires, and a set of rules stating how desires can be achieved, and returns a consistent subset of desires as well as ways/actions for achieving them. Such actions are called intentions. Indeed, we show that these intentions are generated via some decision rules. Thus, depending on whether the agent has an optimistic or a pessimistic attitude, the set of intentions may not be the same.La prise de décision, souvent vue comme une forme de raisonnement sur les actions, a été considérée de différents points de vue. La théorie classique de la décision, développée principalement par des économistes, s’est concentrée sur l’identification et la justification de critères, tels que l’utilité espérée, pour comparer différentes alternatives. Cette approche prend en entrée un ensemble d’actions qui sont atomiques faisables, et une fonction qui évalue les conséquences de chaque action. Un trait remarquable mais aussi une limitation de cette approche est la réduction du problème de décision à la disponibilité de deux fonctions : une fonction de distribution de probabilité et une fonction d’utilité. C’est pourquoi certains chercheurs en IA ont préconisé le besoin d’une approche dans laquelle tous les aspects qui interviennent dans un problème de décision(tels que les désirs d’un agent, la faisabilité desactions, etc..) sont explicitement représentés. Dans cette perspective, des architectures BDI (Beliefs, Desires, Intentions) ont été proposées. Elles prennent leur inspiration dans le travail de philosophes sur ce que les anglo-saxons nomment practical reasoning ou le "raisonnement pratique". Le raisonnement pratique traite principalement de la pertinence au contexte, de la faisabilité et finalement des intentions retenues et exécutables. Cependant, ces approches souffrent d’un manque de formulation claire de règles de décision qui combinent les considérations ci-dessus pour décider quelle action exécuter. Dans cet article, nous montrons que le raisonnement pratique est un problème de la prise de décision généralisé. L’idée fondamentale est qu’au lieu de comparer des actions atomiques, on compare des ensembles d’actions. L’ensemble préféré d’actions devient les intentions retenues par l’un agent. Le papier présente un cadre unifié qui bénéficie des avantages des trois approches (décision classique, architectures BDI, l’idée générales du raisonnement pratique). Plus précisément, nous proposons un cadre formel qui prend en entrée un ensemble de croyances, un ensemble de désirs conditionnels, et un ensemble de règles précisant comment des désirs peuvent être réalisés, et renvoie en sortie un sous-ensemble cohérent de désirs ainsi que les actions pour les réaliser. De telles actions s’appellent les intentions. En effet,nous montrons que ces intentions sont choisies par l’intermédiaire de quelques règles de décision. Ainsi, selon que l’agent ait une attitude optimisteou pessimiste, l’ensemble des intentions peut ne pas être le même

Ranking-based semantics for argumentation frameworks

International audienceAn argumentation system consists of a set of interacting arguments and a semantics for evaluating them. This paper proposes a new family of semantics which rank-orders arguments from the most acceptable to the weakest one(s). The new semantics enjoy two other main features: i) an attack weakens its target but does not kill it, ii) the number of attackers has a great impact on the acceptability of an argument.We start by proposing a set of rational postulates that such semantics could satisfy, then construct various semantics that enjoy them

Equivalence in Logic-Based Argumentation

International audienceThis paper investigates when two abstract logic-based argumentation systems are equivalent. It defines various equivalence criteria, investigates the links between them, and identifies cases where two systems are equivalent with respect to each of the proposed criteria. In particular, it shows that under some reasonable conditions on the logic underlying an argumentation system, the latter has an equivalent finite subsystem, called core. This core constitutes a threshold under which arguments of the system have not yet attained their final status and consequently adding a new argument may result in status change. From that threshold, the statuses of all arguments become stable

An Abstract Framework for Argumentation-based Negotiation

MFI’07 - Actes des Quatrièmes Journées Francophones Modèles formels de l’interactionThis paper proposes an abstract framework for argumentation-based negotiation, in which the role of argumentation is formally analyzed. The framework makes it possible to study the outcomes of an argumentation-based negotiation. It shows what an agreement is, how it is related to the theories of the agents, when it is possible, and how this can be attained by the negotiating agents in this case. It defines also the notion of concession, and shows in which situation an agent will make one, as well as how it influences the evolution of the dialogue

Representing and reasoning about arguments mined from texts and dialogues

International audienceThis paper presents a target language for representing arguments mined from natural language. The key features are the connection between possible reasons and possible claims and recursive embedding of such connections. Given a base of these arguments and counterarguments mined from texts or dialogues, we want be able combine them, deconstruct them, and to analyse them (for instance to check whether the set is inconsistent). To address these needs, we propose a formal language for representing reasons and claims, and a framework for inferencing with the arguments and counterarguments in this formal language