11 citations · 39 across the 22 of their papers we have counts for
12 papers · 1 filter
From User Preferences to Base Score Extraction Functions in Gradual Argumentation (with Appendix)
Aniol Civit, Antonio Rago, Antonio Andriella +2
Gradual argumentation is a field of symbolic AI which is attracting attention for its ability to support transparent and contestable AI systems. It is considered a useful tool in d…
Retrieval- and Argumentation-Enhanced Multi-Agent LLMs for Judgmental Forecasting (Extended Version with Supplementary Material)
Deniz Gorur, Antonio Rago, Francesca Toni
Judgmental forecasting is the task of making predictions about future events based on human judgment. This task can be seen as a form of claim verification, where the claim corresp…
Argumentatively Coherent Judgmental Forecasting
Deniz Gorur, Antonio Rago, Francesca Toni
Judgmental forecasting employs human opinions to make predictions about future events, rather than exclusively historical data as in quantitative forecasting. When these opinions f…
On Gradual Semantics for Assumption-Based Argumentation
Anna Rapberger, Fabrizio Russo, Antonio Rago +1
In computational argumentation, gradual semantics are fine-grained alternatives to extension-based and labelling-based semantics . They ascribe a dialectical strength to (component…
Contestability in Quantitative Argumentation
Xiang Yin, Nico Potyka, Antonio Rago +2
Contestable AI requires that AI-driven decisions align with human preferences. While various forms of argumentation have been shown to support contestability, Edge-Weighted Quantit…
Free Argumentative Exchanges for Explaining Image Classifiers
Avinash Kori, Antonio Rago, Francesca Toni
Deep learning models are powerful image classifiers but their opacity hinders their trustworthiness. Explanation methods for capturing the reasoning process within these classifier…
A Methodology for Incompleteness-Tolerant and Modular Gradual Semantics for Argumentative Statement Graphs
Antonio Rago, Stylianos Loukas Vasileiou, Francesca Toni +2
Gradual semantics (GS) have demonstrated great potential in argumentation, in particular for deploying quantitative bipolar argumentation frameworks (QBAFs) in a number of real-wor…
Exploring the Effect of Explanation Content and Format on User Comprehension and Trust in Healthcare
Antonio Rago, Bence Palfi, Purin Sukpanichnant +5
AI-driven tools for healthcare are widely acknowledged as potentially beneficial to health practitioners and patients, e.g. the QCancer regression tool for cancer risk prediction.…
Heterogeneous Graph Neural Networks with Post-hoc Explanations for Multi-modal and Explainable Land Use Inference
Xuehao Zhai, Junqi Jiang, Adam Dejl +4
Urban land use inference is a critically important task that aids in city planning and policy-making. Recently, the increased use of sensor and location technologies has facilitate…
Explaining Arguments' Strength: Unveiling the Role of Attacks and Supports (Technical Report)
Xiang Yin, Potyka Nico, Francesca Toni
Quantitatively explaining the strength of arguments under gradual semantics has recently received increasing attention. Specifically, several works in the literature provide quanti…
Contestable AI needs Computational Argumentation
Francesco Leofante, Hamed Ayoobi, Adam Dejl +10
AI has become pervasive in recent years, but state-of-the-art approaches predominantly neglect the need for AI systems to be contestable. Instead, contestability is advocated by AI…
The Reasons that Agents Act: Intention and Instrumental Goals
Francis Rhys Ward, Matt MacDermott, Francesco Belardinelli +2
Intention is an important and challenging concept in AI. It is important because it underlies many other concepts we care about, such as agency, manipulation, legal responsibility,…