Gustavo Bodanza

Los marcos de argumentación son modelos de argumentación derrotable desarrollados en el campo de la inteligencia artificial para analizar la justificación de argumentos según su interacción por medio de ataques. Las semánticas de extensiones para los marcos de argumentación son criterios para sancionar uno (punto de vista escéptico) o varios (punto de vista crédulo) subconjuntos de argumentos justificados. Una forma de evaluar diferentes semánticas de extensiones es considerando si satisfacen o no algunos “principios” (es decir, propiedades razonables o deseables). Además de estas semánticas, también se han propuesto enfoques de la argumentación como juegos de diálogos, a modo de teorías de prueba para encontrar argumentos justificados a través de las estrategias ganadoras de un proponente, y se han demostrado correspondencias entre juegos y extensiones. Como consecuencia, los juegos de diálogos argumentativos se pueden poner en correspondencia con los principios de las semánticas de extensiones. Sin embargo, esas correspondencias no siempre se muestran explícitamente. El objetivo de este trabajo es poner de manifiesto tales correspondencias con casos de juegos simples, uno escéptico y dos variantes de crédulos.

El modelo de marcos argumentativos abstractos es actualmente la herramienta más utilizada para caracterizar la justificación de argumentos derrotables en Inteligencia Artificial. Las justificaciones se determinan en base a los ataques entre argumentos y se formalizan a través de semánticas de extensiones. Aquí sostenemos que, o bien algunos marcos argumentativos carecen de sentido bajo ciertas concepciones de ataque específicas, o bien las semánticas más usadas en la literatura, basadas en el concepto de defensa conocido como admisibilidad, no resultan adecuadas para justificar, en particular, argumentos para la toma de decisiones.The abstract argumentation frameworks model is currently the most used tool for characterizing the justification of defeasible arguments in Artificial Intelligence. Justifications are determined on a given attack relation among arguments and are formalized as extension semantics. In this work we argue that, contrariwise to the assumptions in that model, either some argumentation frameworks are meaningless under certain concrete definitions of the attack relation, or some of the most used extension semantics in the literature, based on the defense notion of admissibility, are not suitable in particular for the justification of arguments for decision making.

Dung’s argumentation frameworks are formalisms widely used to model interaction among arguments. Although their study has been profusely developed in the field of Artificial Intelligence, it is not common to see its treatment among those less connected to computer science within the logical-philosophical community. In this paper we propose to bring to that audience a proof-theory for argument justification based on tableaux, very similar to those the Logic students are familiar with. The tableaux enable to calculate whether an argument or subset of arguments are accepted or rejected in accordance to Dung’s preferred and grounded extension-based semantics. Soundness and completeness results regarding those semantics are provided.

We reported a series of experiments carried out to confront the underlying intuitions of value-based argumentation frameworks (VAFs) with the intuitions of ordinary people. Our goal was twofold. On the one hand, we intended to test VAF as a descriptive theory of human argument evaluations. On the other, we aimed to gain new insights from empirical data that could serve to improve VAF as a normative model. The experiments showed that people’s acceptance of arguments deviates from VAF’s semantics and is rather correlated with the importance given to the promoted values, independently of the perceptions of argument interactions through attacks and defeats. Furthermore, arguments were often perceived as promoting more than one value with different relative strengths. Individuals’ analyses of scenarios were also affected by external factors such as biases and arguments not explicit in the framework. Finally, we confirmed that objective acceptance, that is, the acceptance of arguments under any order of the values, was not a frequent behavior. Instead, participants tended to accept only the arguments that promoted the values they subscribe.

$${{{\mathcal {F}}}}$$ -systems are useful digraphs to model sentences that predicate the falsity of other sentences. Paradoxes like the Liar and the one of Yablo can be analyzed with that tool to find graph-theoretic patterns. In this paper we studied this general model consisting of a set of sentences and the binary relation ‘ $$\ldots $$ affirms the falsity of $$\ldots $$ ’ among them. The possible existence of non-referential sentences was also considered. To model the sets of all the sentences that can jointly be valued as true we introduced the notion of conglomerate, the existence of which guarantees the absence of paradox. Conglomerates also enabled us to characterize referential contradictions, i.e., sentences that can only be false under a classical valuation due to the interactions with other sentences in the model. A Kripke-style fixed-point characterization of groundedness was offered, and complete (meaning that every sentence is deemed either true or false) and consistent (meaning that no sentence is deemed true and false) fixed points were put in correspondence with conglomerates. Furthermore, argumentation frameworks are special cases of $$\mathcal{F}$$ -systems. We showed the relation between local conglomerates and admissible sets of arguments and argued about the usefulness of the concept for the argumentation theory.

The abstract argumentation frameworks model is currently the most used tool for characterizing the justification of defeasible arguments in Artificial Intelligence. Justifications are determined on a given attack relation among arguments and are formalized as extension semantics. In this work we argue that, contrariwise to the assumptions in that model, either some argumentation frameworks are meaningless under certain concrete definitions of the attack relation, or some of the most used extension semantics in the literature, based on the defense notion of admissibility, are not suitable in particular for the justification of arguments for decision making.

El modelo de marcos argumentativos abstractos es actualmente la herramienta más utilizada para caracterizar la justificación de argumentos derrotables en Inteligencia Artificial. Las justificaciones se determinan en base a los ataques entre argumentos y se formalizan a través de semánticas de extensiones. Aquí sostenemos que, o bien algunos marcos argumentativos carecen de sentido bajo ciertas concepciones de ataque específicas, o bien las semánticas más usadas en la literatura, basadas en el concepto de defensa conocido como admisibilidad, no resultan adecuadas para justificar, en particular, argumentos para la toma de decisiones.The abstract argumentation frameworks model is currently the most used tool for characterizing the justification of defeasible arguments in Artificial Intelligence. Justifications are determined on a given attack relation among arguments and are formalized as extension semantics. In this work we argue that, contrariwise to the assumptions in that model, either some argumentation frameworks are meaningless under certain concrete definitions of the attack relation, or some of the most used extension semantics in the literature, based on the defense notion of admissibility, are not suitable in particular for the justification of arguments for decision making.

In this paper we present an embedding of abstract argumentation systems into the framework of Barwise and Seligmans logic of information flow. We show that, taking P.M. Dungs characterization of argument systems, a local logic over states of a deliberation may be constructed. In this structure, the key feature of non-monotonicity of commonsense reasoning obtains as the transition from one local logic to another, due to a change in certain background conditions. Each of Dungs extensions of argument systems leads to a corresponding ordering of background conditions. The relations among extensions becomes a relation among partial orderings of background conditions. This introduces a conceptual innovation in Barwise and Seligmans representation of commonsense reasoning.

The problems that arise from the presence of self-attacking ar- guments and odd-length cycles of attack within argumentation frameworks are widely recognized in the literature on defeasible argumentation. This paper introduces two simple semantics to capture different intuitions about what kinds of arguments should become justified in such scenarios. These semantics are modeled upon two extensions of argumentation frameworks, which we call sustainable and tolerant. Each one is constructed on the common ground of the powerful concept of admissibility introduced by Dung in [10]. The novelty of this approach consists in viewing the admissibility of a subset of arguments as relative to potentially challenging subsets of arguments. Both sustainable and tolerant semantics are more credulous than preferred semantics (i.e. they justify at least the same arguments, and possibly more). Given certain sufficient conditions they coincide among them as well as with other semantics introduced by Dung.

Dung’s argumentation frameworks have been applied for over twenty years to the analysis of argument justification. This representation focuses on arguments and the attacks among them, abstracting away from other features like the internal structure of arguments, the nature of utterers, the specifics of the attack relation, etc. The model is highly attractive because it reduces most of the complexities involved in argumentation processes. It can be applied to different settings, like the argument evaluation of an individual agent or the case of dialectic disputes between two agents (pro and con), or even in multi-agent collective argumentation. The latter case involves agents with possibly different arguments and/or opinions on how to evaluate them, leading to the possibility of considering multiple sets of arguments and attack relations. Two basic questions can be asked here, namely ‘what to aggregate’ and ‘how to aggregate’. The former concerns what kinds of entities do the agents intend to choose (arguments, attacks, assessments, etc.), while the second one focuses on which aggregation mechanisms yield rational choices (voting on arguments, merging procedures to obtain a common argumentation framework, deliberation processes, etc.). In particular, the question about the rationality of a collective argument choice relates this topic to Social Choice and Judgment Aggregation theories, while its associated strategic issues relate it to Game Theory. The research efforts on the disparate problems elicited by collective argumentation have generated a considerable corpus of literature that deserves an orderly evaluation. This survey is intended as a contribution to that end.

This work introduces a system of suppositional argumentation , trying to give a foundation for dealing intuitively with disjunctive information in a defeasible reasoning framework. Defeasible argumentation systems proposed in the field of Artificial Intelligence lack in general of such a capability. Our view is that suppositional reasoning is present in defeasible arguments involving disjunctions, just as in reasoning by cases in classical logic. Disjunctive information can express different plausible alternatives which consideration would improve the results of a debate. Here is studied in what extent an argument assuming such plausible alternative can be considered relevant within the given context, and how those alternatives can be compared on basis of their explicative power. In consequence, a debate can be affected in several aspects, among which counter argumentation, defeat and justification have to be considered. Moreover, a comparison among arguments using specificity is adopted, obtaining that also defeasible contrapositive arguments are treated intuitively. Interesting properties of the system are proved, and common sense rationality is tested with several benchmark problems