Extending Modular Semantics for Bipolar Weighted Argumentation (Technical Report)

Weighted bipolar argumentation frameworks offer a tool for decision support and social media analysis. Arguments are evaluated by an iterative procedure that takes initial weights and attack and support relations into account. Until recently, convergence of these iterative procedures was not very well understood in cyclic graphs. Mossakowski and Neuhaus recently introduced a unification of different approaches and proved first convergence and divergence results. We build up on this work, simplify and generalize convergence results and complement them with runtime guarantees. As it turns out, there is a tradeoff between semantics' convergence guarantees and their ability to move strength values away from the initial weights. We demonstrate that divergence problems can be avoided without this tradeoff by continuizing semantics. Semantically, we extend the framework with a Duality property that assures a symmetric impact of attack and support relations. We also present a Java implementation of modular semantics and explain the practical usefulness of the theoretical ideas

Assessing the Impact of Agents in Weighted Bipolar Argumentation Frameworks

Postprin

SpArX: Sparse Argumentative Explanations for Neural Networks

Neural networks (NNs) have various applications in AI, but explaining their decision process remains challenging. Existing approaches often focus on explaining how changing individual inputs affects NNs' outputs. However, an explanation that is consistent with the input-output behaviour of an NN is not necessarily faithful to the actual mechanics thereof. In this paper, we exploit relationships between multi-layer perceptrons (MLPs) and quantitative argumentation frameworks (QAFs) to create argumentative explanations for the mechanics of MLPs. Our SpArX method first sparsifies the MLP while maintaining as much of the original mechanics as possible. It then translates the sparse MLP into an equivalent QAF to shed light on the underlying decision process of the MLP, producing global and/or local explanations. We demonstrate experimentally that SpArX can give more faithful explanations than existing approaches, while simultaneously providing deeper insights into the actual reasoning process of MLPs

Inferring Attack Relations for Gradual Semantics

Peer reviewedPublisher PD

The Inverse Problem for Argumentation Gradual Semantics

Preprin

Evaluation of Analogical Arguments by Choquet Integral

Analogical arguments are a special type of inductive arguments, whereby perceived similarities are used as a basis to infersome further similarity that has yet to be observed. Although they arenot deductively valid, they may yield conclusions that are very prob-ably true, and may be more cogent than others in persuasive contexts.This paper tackles the question of their evaluation. It starts by dis-cussing their features, how they can be attacked/supported, and keyconsiderations for their evaluation. It argues in particular for the needof semantics that are able to take into account possible interactions(synergies, redundancies) between attackers (respectively support-ers) of any analogical argument. It presents principles that serve asguidelines for choosing candidate semantics. Then, it shows that ex-isting (extension, gradual, ranking) semantics are not suitable as theymay lead to inaccurate assessments. Finally, it redefines three exist-ing semantics using the well-known Choquet Integral for aggregatingattackers/supporter, and discusses their properties