Decision making and quality-of-information

Springer - Series Advances in Intelligent and Soft Computing, vol. 73In Group Decision Making based on argumentation, decisions are made considering the diverse points of view of the different partakers in order to decide which course of action a group should follow. However, knowledge and belief are normally incomplete, contradictory, or error sensitive, being desirable to use formal tools to deal with the problems that arise from the use of uncertain and even not precise information. On the other hand, qualitative models and qualitative reasoning have been around in Artificial Intelligence research for some time, in particular due the growing need to offer support in decision-making processes, a problem that in this work will be addressed in terms of an extension to the logic programming language and based on an evaluation of the Quality-of-Information (QoI) that stems out from those extended logic programs or theories. We present a computational model to address the problem of decision making, in terms of a multitude of scenarios, also defined as logic programs or theories, where the more appropriate ones stand for the higher QoIs values

Building bridges between doctors and patients: the design and pilot evaluation of a training session in argumentation for chronic pain experts

Shared decision-making requires doctors to be competent in exchanging views with patients to identify the appropriate course of action. In this paper we focus on the potential of a course in argumentation as a promising way to empower doctors in presenting their viewpoints and addressing those of patients. Argumentation is the communication process in which the speaker, through the use of reasons, aims to convince the interlocutor of the acceptability of a viewpoint. The value of argumentation skills for doctors has been addressed in the literature. Yet, there is no research on what a course on argumentation might look like. In this paper, we present the content and format of a training session in argumentation for doctors and discuss some insights gained from a pilot study that examined doctors' perceived strengths and limitations vis-à-vis this training

A logical framework for modeling argumentation using labelled deduction

Classical methods for representing and reasoning with knowledge rely on the assumption that the available information is complete, certain and consistent. In real-world problems this is usually not the case, and Al has long dealt with the issue of finding a suitable formalization for commonsense reasoning. Defeasible argumentation [SL92, CMLOO, PV99] has proven to be a successful approach in many respects, since it naturally resembles many aspects of human commonsense reasoning. Our intention is to find a logical framework in which the diverse aspects of defeasible argumentation can be formally captured, in order to analyze their emerging properties. The issue of defining a logical framework for defeasible argumentation with labels has been tackled before in alternative ways. Hunter proposed a framework for characterizing structural information using labelled formulas combined with argumentation [Hun94]. Fox & Parsons [FP97] defined a Logic of Argumentation, a qualitative approach to decision making which makes use of labelled formulac, presented as an altemative to standard formalisms in order to overcome some of the limitations imposed by them. Our approach focuses on formalizing an argunentative framework using Defeasible Logic Programming [Gar97] as a theoretical basis, combined with labelled deductive systems [Gab96]. In this presentation we describe the main aspects of our formalization.Eje: Aspectos teóricos de inteligencia artificialRed de Universidades con Carreras en Informática (RedUNCI

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

The role of emotions and conflicting online reviews on consumers' purchase intentions

Drawing on dual-process theories, this paper explains how the systematic and heuristic information processing of online reviews with conflicting information can influence consumers' purchase decision making. The study adopts major assumptions of complexity and configuration theory in employing fuzzy-set qualitative comparative analysis on 680 TripAdvisor users to test the complex interrelationships between emotions and the systematic and heuristic cues used in processing reviews. The results show that the systematic and heuristic processing of online reviews can produce independent impacts on consumer decision making. Both processing routes can interact with each other to affect the domination of one route over the other. In the case of a positive–negative sequence, consumers mainly follow a heuristic processing route. In the reverse sequence, consumers' concerns about the credibility of the reviews leads them to think more deeply (systematic processing) and actively evaluate both the argumentation quality and the helpfulness of the online reviews