Argumentation has been an important topic in knowledge representation, reasoning and multi-agent systems during the last twenty years. In this paper, we propose a new abstract framework where arguments are associated with a strength, namely a quantitative information which is used to determine whether an attack between arguments succeeds or not. Our Strength-based Argumentation Framework combines ideas of Preference-based and Weighted Argumentation Frameworks in an original way, which permits to define acceptability semantics sensitive to the existence of accruals between arguments. (...) The question of accruals arises in situations where several arguments defending the same position against another argument are unable to individually defeat this argument, but could do it collectively if they combine their strengths. We investigate some of the theoretical and computational properties of our new framework and semantics, and present a reasoning algorithm that is based on a translation of the problem into pseudo-boolean constraint satisfaction. This paper proposes an intuitive framework which allows strength compensations in an argumentation context where attacks may not succeed, completed by an approach which detects accruals throughout the reasoning process without requiring the elicitation of all compensatory combinations of arguments as an input. (shrink)

1. The benefits resulting from a combination of quantitative (e.g. probabilistic) and qualitative (e.g. logic-based) reasoning are widely acknowledged (e.g. see Domingos et al., 2006; Poole, 2011)....

One of the challenging problems in large scale cyber-argumentation platforms is that users often engage and focus only on a few issues and leave other issues under-discussed and under-acknowledged. This kind of non-uniform participation obstructs the argumentation analysis models to retrieve collective intelligence from the underlying discussion. To resolve this problem, we developed an innovative opinion prediction model for a multi-issue cyber-argumentation environment. Our model predicts users’ opinions on the non-participated issues from similar users’ opinions on related issues using intelligent (...) argumentation techniques and a collaborative filtering method. Based on our detailed experimental results on an empirical dataset collected using our cyber-argumentation platform, our model is 21.7% more accurate, handles data sparsity better than other popular opinion prediction methods. Our model can also predict opinions on multiple issues simultaneously with reasonable accuracy. Contrary to existing opinion prediction models, which only predict whether a user agrees on an issue, our model predicts how much a user agrees on the issue. To our knowledge, this is the first research to attempt multi-issue opinion prediction with the partial agreement in the cyber-argumentation platform. With additional data on non-participated issues, our opinion prediction model can help the collective intelligence analysis models to analyze social phenomena more effectively and accurately in the cyber argumentation platform. (shrink)