Value-based argumentation frameworks as neural-symbolic learning systems

While neural networks have been successfully used in a number of machine learning applications, logical languages have been the standard for the representation of argumentative reasoning. In this paper, we establish a relationship between neural networks and argumentation networks, combining reasoning and learning in the same argumentation framework. We do so by presenting a new neural argumentation algorithm, responsible for translating argumentation networks into standard neural networks. We then show a correspondence between the two networks. The algorithm works not only for acyclic argumentation networks, but also for circular networks, and it enables the accrual of arguments through learning as well as the parallel computation of arguments

Solving Power and Trust Conflicts through Argumentation in Agent-mediated Knowledge Distribution

Distributing pieces of knowledge in large, usually distributed organizations is a central problem in Knowledge and Organization management. Policies for distributing knowledge and information are mostly incomplete or in potential conflict with each other. As a consequence, decision processes for information distribution may be difficult to formalize on the basis of a rationally justified procedure. This article presents an argumentative approach to cope with this problem based on integrating the JITIK multiagent system with Defeasible Logic Programming (DeLP), a logic programming formalism for defeasible argumentation. We show how power relations, as well as delegation and trust, can be embedded within our framework in terms of DeLP, in such a way that a dialectical argumentation process works as a decision core. Conflicts among policies are solved on the basis of a dialectical analysis whose outcome determines to which specific users different pieces of knowledge are to be delivered.Fil: Chesñevar, Carlos Iván. Universitat de Lleida; España. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; ArgentinaFil: Brena, Ramón. Centro de Sistemas Inteligentes, Tecnológico de Monterrey; MéxicoFil: Aguirre, José L.. Centro de Sistemas Inteligentes, Tecnológico de Monterrey; Méxic

Stratified Labelings for Abstract Argumentation

We introduce stratified labelings as a novel semantical approach to abstract argumentation frameworks. Compared to standard labelings, stratified labelings provide a more fine-grained assessment of the controversiality of arguments using ranks instead of the usual labels in, out, and undecided. We relate the framework of stratified labelings to conditional logic and, in particular, to the System Z ranking functions

Arg-tuProlog: A tuProlog-based argumentation framework

Over the last decades, argumentation has become increasingly central as a frontier research within artificial intelligence (AI), especially around the notions of interpretability and explainability, which are more and more required within AI applications. In this paper we present the first prototype of Arg-tuProlog, a logic-based argumentation tool built on top of the tuProlog system. In particular, Arg-tuProlog enables defeasible reasoning and argumentation, and deals with priorities over rules. It also includes a formal method for dealing with burden of proof (burden of persuasion). Being lightweight and compliant to the requirements for micro-intelligence, Arg-tuProlog is perfectly suited for injecting argumentation into distributed pervasive systems

Argumentation for machine learning: a survey

Existing approaches using argumentation to aid or improve machine learning differ in the type of machine learning technique they consider, in their use of argumentation and in their choice of argumentation framework and semantics. This paper presents a survey of this relatively young field highlighting, in particular, its achievements to date, the applications it has been used for as well as the benefits brought about by the use of argumentation, with an eye towards its future