Computing Argument Preferences and Explanations in Abstract Argumentation

Financial support from The UK Engineering and Physical Sciences Research Council (EPSRC) for the grant (EP/P011829/1), Supporting Security Policy with Effective Digital Intervention (SSPEDI) is gratefully acknowledged.Postprin

Argument mining: A machine learning perspective

Argument mining has recently become a hot topic, attracting the interests of several and diverse research communities, ranging from artificial intelligence, to computational linguistics, natural language processing, social and philosophical sciences. In this paper, we attempt to describe the problems and challenges of argument mining from a machine learning angle. In particular, we advocate that machine learning techniques so far have been under-exploited, and that a more proper standardization of the problem, also with regards to the underlying argument model, could provide a crucial element to develop better systems

A survey on managing users' preferences in ambient intelligence

Understanding the importance of preference management in ambient intelligent environments is key to providing systems that are better prepared to meet users' expectations. This survey provides an account of the various ways that preferences have been handled in Artificial Intelligence. Our analysis indicates that most of those techniques lack the ability to handle ambiguity and the evolution of preferences over time. Further exploration shows that argumentation can provide a feasible solution to complement existing work. We illustrate our claim by using an intelligent environment case study

Belief Revision and Computational Argumentation: A Critical Comparison

This paper aims at comparing and relating belief revision and argumentation as approaches to model reasoning processes. Referring to some prominent literature references in both fields, we will discuss their (implicit or explicit) assumptions on the modeled processes and hence commonalities and differences in the forms of reasoning they are suitable to deal with. The intended contribution is on one hand assessing the (not fully explored yet) relationships between two lively research fields in the broad area of defeasible reasoning and on the other hand pointing out open issues and potential directions for future research

Explaining Bayesian Networks using Argumentation

Abstract. Qualitative and quantitative systems to deal with uncer-tainty coexist. Bayesian networks are a well known tool in probabilistic reasoning. For non-statistical experts, however, Bayesian networks may be hard to interpret. Especially since the inner workings of Bayesian networks are complicated they may appear as black box models. Ar-gumentation approaches, on the contrary, emphasise the derivation of results. Argumentation models, however, have notorious difficulty dealing with probabilities. In this paper we formalise a two-phase method to extract probabilistically supported arguments from a Bayesian network. First, from a BN we construct a support graph, and, second, given a set of observations we build arguments from that support graph. Such arguments can facilitate the correct interpretation and explanation of the evidence modelled in the Bayesian network

Handling disagreement in ontologies-based reasoning via argumentation

International audienceOntologies are at the heart of the Semantic Web technologies. This paper introduces a framework for reasoning under uncertainty in the context of ontologies represented in description logics; these ontologies could be inconsis- tent or incoherent. Conflicts are addressed through a form of logic-based argu- mentation. We examine how the number of attacks and the weights of arguments can be used to define various labelling functions that identify the justification statuses of arguments. Then, different inference relations are distinguished to obtain meaningful answers to queries from imperfect ontologies without extra computational costs compared to classical DL reasoning. Lastly, we study the properties of these new entailment relations and their relationships with other well-known existing ones

A probabilistic author-centered model for Twitter discussions

In a recent work some of the authors have developed an argumentative approach for discovering relevant opinions in Twitter discussions with probabilistic valued relationships. Given a Twitter discussion, the system builds an argument graph where each node denotes a tweet and each edge denotes a criticism relationship between a pair of tweets of the discussion. Relationships between tweets are associated with a probability value, indicating the uncertainty that the relationships hold. In this work we introduce and investigate a natural extension of the representation model, referred as probabilistic author-centered model, in which tweets within a discussion are grouped by authors, in such a way that tweets of a same author describe his/her opinion in the discussion and are rep- resented with a single node in the graph, and criticism relationships denote controversies between opinions of Twitter users in the discussion. In this new model, the interactions between authors can give rise to circular criticism relationships, and the probability of one opinion criticizing another has to be evaluated from the probabilities of criticism among the tweets that compose both opinions.This work was partially funded by the Spanish MICINN Projects TIN2015-71799-C2-1-P and TIN2015-71799-C2-2-PPeer Reviewe