Self-reported price of cigarettes, consumption and compensatory behaviours in a cohort of Mexican smokers before and after a cigarette tax increase

This paper presents a novel SAT-based approach for the computation of extensions in abstract argumentation, with focus on preferred semantics, and an empirical evaluation of its performances. The approach is based on the idea of reducing the problem of computing complete extensions to a SAT problem and then using a depth-first search method to derive preferred extensions. The proposed approach has been tested using two distinct SAT solvers and compared with three state-of-the-art systems for preferred extension computation. It turns out that the proposed approach delivers significantly better performances in the large majority of the considered cases

Hervorming van het fiscale instrumentarium voor inkomensbeleid

Hervorming Sociale Regelgevin

Persuasive argumentation and epistemic attitudes

These slides present the main notions and results of a work under construction that was presented in the 2nd DaLí Workshop, Dynamic Logic: New Trends and Applications in Porto, 9 October, 2019 and later published in the Lectures Notes in Computer Science (vol 12005). The work develops a formal study of persuasive dialogues among individuals, taking into account the epistemic attitudes of the involved agents. Abstract argumentation and dynamic epistemic logic provide the necessary tools for such an analysis. The interested reader is referred to the paper for further detailsUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Introducing Preference-Based Argumentation to Inconsistent Ontological Knowledge Bases

International audienceHandling inconsistency is an inherent part of decision making in traditional agri-food chains – due to the various concerns involved. In order to explain the source of inconsistency and represent the existing conflicts in the ontological knowledge base, argumentation theory can be used. However, the current state of art methodology does not allow to take into account the level of significance of the knowledge expressed by the various ontological knowledge sources. We propose to use preferences in order to model those differences between formulas and evaluate our proposal practically by implementing it within the INRA platform and showing a use case using this formalism in a bread making decision support system

Computing Consensus: A Logic for Reasoning About Deliberative Processes Based on Argumentation

Argumentation theory can encode an agent’s assessment of the state of an exchange of points of view. We present a conservative model of multiple agents potentially disagreeing on the views presented during a process of deliberation. We model this process as iteratively adding points of view (arguments), or aspects of points of view. This gives rise to a modal logic, deliberative dynamic logic, which permits us to reason about the possible developments of the deliberative state. The logic we propose applies to all natural semantics of argumentation theory. Furthermore, under a very weak assumption that the consensus considered by a group of agents is faithful to their individual views, we show that model checking these models is feasible, as long as the argumentation frameworks, which may be infinite, does not have infinite branching.acceptedVersio

A neural cognitive model of argumentation with application to legal inference and decision making

Formal models of argumentation have been investigated in several areas, from multi-agent systems and artificial intelligence (AI) to decision making, philosophy and law. In artificial intelligence, logic-based models have been the standard for the representation of argumentative reasoning. More recently, the standard logic-based models have been shown equivalent to standard connectionist models. This has created a new line of research where (i) neural networks can be used as a parallel computational model for argumentation and (ii) neural networks can be used to combine argumentation, quantitative reasoning and statistical learning. At the same time, non-standard logic models of argumentation started to emerge. In this paper, we propose a connectionist cognitive model of argumentation that accounts for both standard and non-standard forms of argumentation. The model is shown to be an adequate framework for dealing with standard and non-standard argumentation, including joint-attacks, argument support, ordered attacks, disjunctive attacks, meta-level attacks, self-defeating attacks, argument accrual and uncertainty. We show that the neural cognitive approach offers an adequate way of modelling all of these different aspects of argumentation. We have applied the framework to the modelling of a public prosecution charging decision as part of a real legal decision making case study containing many of the above aspects of argumentation. The results show that the model can be a useful tool in the analysis of legal decision making, including the analysis of what-if questions and the analysis of alternative conclusions. The approach opens up two new perspectives in the short-term: the use of neural networks for computing prevailing arguments efficiently through the propagation in parallel of neuronal activations, and the use of the same networks to evolve the structure of the argumentation network through learning (e.g. to learn the strength of arguments from data)