activity
20202022
most citedExplaining Causal Models with Argumentation: the Case of Bi-variate Reinforcement

1 citations · 2 across the 3 of their papers we have counts for

collaborators

6 papers

cs.AI20221 cited

Forecasting Argumentation Frameworks

Benjamin Irwin, Antonio Rago, Francesca Toni

We introduce Forecasting Argumentation Frameworks (FAFs), a novel argumentation-based methodology for forecasting informed by recent judgmental forecasting research. FAFs comprise…

cs.AI20221 cited

Explaining Causal Models with Argumentation: the Case of Bi-variate Reinforcement

Antonio Rago, Pietro Baroni, Francesca Toni

Causal models are playing an increasingly important role in machine learning, particularly in the realm of explainable AI. We introduce a conceptualisation for generating argumenta…

cs.AI2021

Argumentative XAI: A Survey

Kristijonas Čyras, Antonio Rago, Emanuele Albini +2

Explainable AI (XAI) has been investigated for decades and, together with AI itself, has witnessed unprecedented growth in recent years. Among various approaches to XAI, argumentat…

hep-lat2021

Ergodic sampling of the topological charge using the density of states

Guido Cossu, David Lancaster, Biagio Lucini +2

In lattice calculations, the approach to the continuum limit is hindered by the severe freezing of the topological charge, which prevents ergodic sampling in configuration space. I…

cs.AI2020

Influence-Driven Explanations for Bayesian Network Classifiers

Antonio Rago, Emanuele Albini, Pietro Baroni +1

One of the most pressing issues in AI in recent years has been the need to address the lack of explainability of many of its models. We focus on explanations for discrete Bayesian…

cs.AI2020

Deep Argumentative Explanations

Emanuele Albini, Piyawat Lertvittayakumjorn, Antonio Rago +1

Despite the recent, widespread focus on eXplainable AI (XAI), explanations computed by XAI methods tend to provide little insight into the functioning of Neural Networks (NNs). We…