A Framework for Combining Defeasible Argumentation with Labeled Deduction

In the last years, there has been an increasing demand of a variety of logical systems, prompted mostly by applications of logic in AI and other related areas. Labeled Deductive Systems (LDS) were developed as a flexible methodology to formalize such a kind of complex logical systems. Defeasible argumentation has proven to be a successful approach to formalizing commonsense reasoning, encompassing many other alternative formalisms for defeasible reasoning. Argument-based frameworks share some common notions (such as the concept of argument, defeater, etc.) along with a number of particular features which make it difficult to compare them with each other from a logical viewpoint. This paper introduces LDSar, a LDS for defeasible argumentation in which many important issues concerning defeasible argumentation are captured within a unified logical framework. We also discuss some logical properties and extensions that emerge from the proposed framework.Comment: 15 pages, presented at CMSRA Workshop 2003. Buenos Aires, Argentin

Combining quantitative and qualitative reasoning in defeasible argumentation

Labeled Deductive Systems (LDS) were developed as a rigorous but exible method- ology to formalize complex logical systems, such as temporal logics, database query languages and defeasible reasoning systems. LDSAR is a LDS-based framework for defeasible argumentation which subsumes di erent existing argumentation frameworks, providing a testbed for the study of dif- ferent relevant features (such as logical properties and ontological aspects, among others). This paper presents LDS AR, an extension of LDSAR that incorporates the ability to combine quantitative and qualitative features within a uni ed argumentative setting. Our approach involves the assignment of certainty factors to formulas in the knowl- edge base. These values are propagated when performing argumentative inference, o ering an alternative source of information for evaluating the strength of arguments in the dialectical analysis. We will also discuss some emerging logical properties of the resulting framework.Eje: Lógica e Inteligencia artificialRed de Universidades con Carreras en Informática (RedUNCI

Combining quantitative and qualitative reasoning in defeasible argumentation

Labeled Deductive Systems (LDS) were developed as a rigorous but exible method- ology to formalize complex logical systems, such as temporal logics, database query languages and defeasible reasoning systems. LDSAR is a LDS-based framework for defeasible argumentation which subsumes di erent existing argumentation frameworks, providing a testbed for the study of dif- ferent relevant features (such as logical properties and ontological aspects, among others). This paper presents LDS AR, an extension of LDSAR that incorporates the ability to combine quantitative and qualitative features within a uni ed argumentative setting. Our approach involves the assignment of certainty factors to formulas in the knowl- edge base. These values are propagated when performing argumentative inference, o ering an alternative source of information for evaluating the strength of arguments in the dialectical analysis. We will also discuss some emerging logical properties of the resulting framework.Eje: Lógica e Inteligencia artificialRed de Universidades con Carreras en Informática (RedUNCI

Improving argumentation-based recommender systems through context-adaptable selection criteria

Recommender Systems based on argumentation represent an important proposal where the recommendation is supported by qualitative information. In these systems, the role of the comparison criterion used to decide between competing arguments is paramount and the possibility of using the most appropriate for a given domain becomes a central issue; therefore, an argumentative recommender system that offers an interchangeable argument comparison criterion provides a significant ability that can be exploited by the user. However, in most of current recommender systems, the argument comparison criterion is either fixed, or codified within the arguments. In this work we propose a formalization of context-adaptable selection criteria that enhances the argumentative reasoning mechanism. Thus, we do not propose of a new type of recommender system; instead we present a mechanism that expand the capabilities of existing argumentation-based recommender systems. More precisely, our proposal is to provide a way of specifying how to select and use the most appropriate argument comparison criterion effecting the selection on the user´s preferences, giving the possibility of programming, by the use of conditional expressions, which argument preference criterion has to be used in each particular situation.Fil: Teze, Juan Carlos Lionel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional de Entre Ríos; ArgentinaFil: Gottifredi, Sebastián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; ArgentinaFil: García, Alejandro Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; ArgentinaFil: Simari, Guillermo Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; Argentin

A Labelling Framework for Probabilistic Argumentation

The combination of argumentation and probability paves the way to new accounts of qualitative and quantitative uncertainty, thereby offering new theoretical and applicative opportunities. Due to a variety of interests, probabilistic argumentation is approached in the literature with different frameworks, pertaining to structured and abstract argumentation, and with respect to diverse types of uncertainty, in particular the uncertainty on the credibility of the premises, the uncertainty about which arguments to consider, and the uncertainty on the acceptance status of arguments or statements. Towards a general framework for probabilistic argumentation, we investigate a labelling-oriented framework encompassing a basic setting for rule-based argumentation and its (semi-) abstract account, along with diverse types of uncertainty. Our framework provides a systematic treatment of various kinds of uncertainty and of their relationships and allows us to back or question assertions from the literature

Dealing with Qualitative and Quantitative Features in Legal Domains

In this work, we enrich a formalism for argumentation by including a formal characterization of features related to the knowledge, in order to capture proper reasoning in legal domains. We add meta-data information to the arguments in the form of labels representing quantitative and qualitative data about them. These labels are propagated through an argumentative graph according to the relations of support, conflict, and aggregation between arguments.Comment: arXiv admin note: text overlap with arXiv:1903.0186