We show that an interesting class of feed-forward neural networks can be understood as
quantitative argumentation frameworks. This connection creates a bridge between research …

Neural networks (NNs) have various applications in AI, but explaining their decisions
remains challenging. Existing approaches often focus on explaining how changing …

This paper presents a formal approach to explaining change of inference in Quantitative
Bipolar Argumentation Frameworks (QBAFs). When drawing conclusions from a QBAF and …

We present a principle-based analysis of contribution functions for quantitative bipolar
argumentation graphs that quantify the contribution of one argument to another. The …

Extending Modular Semantics for Bipolar Weighted Argumentation (Extended Abstract) |
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Gradual semantics with abstract argumentation provide each argument with a score
reflecting its acceptability. Many different gradual semantics have been proposed in the …

Argumentative explainable AI has been advocated by several in recent years, with an
increasing interest on explaining the reasoning outcomes of Argumentation Frameworks …

Weighted knowledge bases for description logics with typicality provide a logical
interpretation of MultiLayer Perceptrons, based on a “concept-wise” multi-preferential …

Weighted bipolar argumentation frameworks allow modeling decision problems and online
discussions by defining arguments and their relationships. The strength of arguments can be …

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 …