A Comparative Study of Ranking-based Semantics for Abstract Argumentation

Argumentation is a process of evaluating and comparing a set of arguments. A way to compare them consists in using a ranking-based semantics which rank-order arguments from the most to the least acceptable ones. Recently, a number of such semantics have been proposed independently, often associated with some desirable properties. However, there is no comparative study which takes a broader perspective. This is what we propose in this work. We provide a general comparison of all these semantics with respect to the proposed properties. That allows to underline the differences of behavior between the existing semantics.Comment: Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI-2016), Feb 2016, Phoenix, United State

Integrating defeasible argumentation and machine learning techniques : Preliminary report

The field of machine learning (ML) is concerned with the question of how to construct algorithms that automatically improve with experience. In recent years many successful ML applications have been developed, such as datamining programs, information-filtering systems, etc. Although ML algorithms allow the detection and extraction of interesting patterns of data for several kinds of problems, most of these algorithms are based on quantitative reasoning, as they rely on training data in order to infer so-called target functions. In the last years defeasible argumentation has proven to be a sound setting to formalize common-sense qualitative reasoning. This approach can be combined with other inference techniques, such as those provided by machine learning theory. In this paper we outline different alternatives for combining defeasible argumentation and machine learning techniques. We suggest how different aspects of a generic argumentbased framework can be integrated with other ML-based approaches.Eje: Inteligencia artificialRed de Universidades con Carreras en Informática (RedUNCI

ArgFrame: A Multi-Layer, Web, Argument-Based Framework for Quantitative Reasoning

Multiple systems have been proposed to perform computational argumentation activities, but there is a lack of options for dealing with quantitative inferences. This multi-layer, web, argument-based framework has been proposed as a tool to perform automated reasoning with numerical data. It is able to use boolean logic for the creation of if-then rules and attacking rules. In turn, these rules/arguments can be activated or not by some input data, have their attacks solved (following some Dung or rank-based semantics), and finally aggregated in different fashions in order to produce a prediction (a number). The framework is implemented in PHP for the back-end. A JavaScript interface is provided for creating arguments, attacks among arguments, and performing case-by-case analyses