Logical models of arguement formalize commonsense reasoning while taking process and computation seriously. This survey discusses the main ideas that characterize different logical models of argument. It presents the formal features of a few features of a few main approaches to the modeling of argumentation. We trace the evolution of argumentation from the mid-1980s, when argument systems emerged as an alternative to nonmonotonic formalisms based on classical logic, to the present, as argument in embedded in different complex systems for real-world applications, and allow more formal work to be done in different areas, such as AI and Law, case-based reasoning and negotiation among intelligent agents.

The theory of argumentation is a rich, interdisciplinary area of research straddling the fields of artificial intelligence, philosophy, communication studies, linguistics and psychology. In the last few years, significant progress has been made in understanding the theoretical properties of different argumentation logics. However, one major barrier to the development and practical deployment of argumentation systems is the lack of a shared, agreed notation or ‘interchange format’ for argumentation and arguments. In this paper, we describe a draft specification for an argument interchange format (AIF) intended for representation and exchange of data between various argumentation tools and agent-based applications. It represents a consensus ‘abstract model’ established by researchers across fields of argumentation, artificial intelligence and multi-agent systems. In its current form, this specification is intended as a starting point for further discussion and elaboration by the community, rather than an attempt at a definitive, all-encompassing model. However, to demonstrate proof of concept, a use case scenario is briefly described. Moreover, three concrete realizations or ‘reifications’ of the abstract model are illustrated.

In the last decade defeasible argumentation frameworks have evolved to become a sound setting to formalize commonsense, qualitative reasoning. The logic programming paradigm has shown to be particularly useful for developing different argument-based frameworks on the basis of different variants of logic programming which incorporate defeasible rules. Most of such frameworks, however, are unable to deal with both explicit uncertainty and vague knowledge, as defeasibility is directly encoded in the object language. This paper presents possibilistic defeasible logic programming (P-DeLP), a new logic programming language which combines features from argumentation theory and logic programming, incorporating as well the treatment of possibilistic uncertainty. Such features are formalized on the basis of PGL, a possibilistic logic based on Gödel fuzzy logic. One of the applications of P-DeLP is providing an intelligent agent with non-monotonic, argumentative inference capabilities. In this paper we also provide a better understanding of such capabilities by defining two non-monotonic operators which model the expansion of a given program by adding new weighed facts associated with argument conclusions and warranted literals, respectively. Different logical properties for the proposed operators are studied. © 2008 Elsevier B.V. All rights reserved.This research was partially supported by CYCYT Projects MULOG (TIN2004-07933-C03-01/03) and IEA (TIN2006-15662-C02-01/02), by CONICET (Argentina), by the Secretaría General de Ciencia y Tecnología de la Universidad Nacional del Sur (Project 24/ZN10), and by Agencia Nacional de Promoción Científica y Tecnológica (PICT 2002 No. 13096).Peer Reviewe

Automating customer complaints processing is a major issue in the context of knowledge management technologies for most companies nowadays. Automated decision-support systems are important for complaint processing, integrating human experience in understanding complaints and the application of machine learning techniques. In this context, a major challenge in complaint processing involves assessing the validity of a customer complaint on the basis of the emerging dialogue between a customer and a company representative. This paper presents a novel approach for modelling and classifying complaint scenarios associated with customer-company dialogues. Such dialogues are formalized as labelled graphs, in which both company and customer interact through communicative actions, providing arguments that support their points. We show that such argumentation provides a complement to perform machine learning reasoning on communicative actions, improving the resulting classification accuracy

Most formalisms for representing common-sense knowledge allow incomplete and potentially inconsistent information. When strong negation is also allowed, contradictory con- clusions can arise. A criterion for deciding between them is needed. The aim of this paper is to investigate an inherent and autonomous comparison criterion, based on specificity as defined in (POO 85, SIM 92). In contrast to other approaches, we consider not only defeasible, but also strict knowledge. Our criterion is context-sensitive, i. e., preference among defeasible rules is determined dynamically during the dialectical analysis. We show how specificity can be defined in terms of two different approaches:activation sets and derivation trees. This allows us to get a syntactic criterion that can be implemented in a computationally attractive way. The resulting definitions may be applied in general rule-based formalisms. We present theorems linking both characterizations. Finally we discuss other frame- works for defeasible reasoning in which preference handling is considered explicitly.