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Fabrizio Russo

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6 papers
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6

AAAI Conference 2026 Conference Paper

Heterogeneous Graph Neural Networks for Assumption-Based Argumentation

  • Preesha Gehlot
  • Anna Rapberger
  • Fabrizio Russo
  • Francesca Toni

Assumption‐Based Argumentation (ABA) is a powerful structured argumentation formalism, but exact computation of extensions under stable semantics is intractable for large frameworks. We present the first Graph Neural Network (GNN) approach to approximate credulous acceptance in ABA. To leverage GNNs, we model ABA frameworks via a dependency graph representation encoding assumptions, claims and rules as nodes, with heterogeneous edge labels distinguishing support, derive and attack relations. We propose two GNN architectures—ABAGCN and ABAGAT—that stack residual heterogeneous convolution or attention layers, respectively, to learn node embeddings. Our models are trained on the ICCMA 2023 benchmark, augmented with synthetic ABAFs, with hyperparameters optimised via Bayesian search. Empirically, both ABAGCN and ABAGAT outperform a state‐of‐the‐art GNN baseline that we adapt from the abstract argumentation iterature, achieving a node‐level F1 score of up to 0.71 on the ICCMA instances. Finally, we develop a sound polynomial time extension‐reconstruction algorithm driven by our predictor: it reconstructs stable extensions with F1 above 0.85 on small ABAFs and maintains an F1 of about 0.58 on large frameworks. Our work opens new avenues for scalable approximate reasoning in structured argumentation.

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KR Conference 2025 Conference Paper

On Gradual Semantics for Assumption-Based Argumentation

  • Anna Rapberger
  • Fabrizio Russo
  • Antonio Rago
  • Francesca Toni

In computational argumentation, gradual semantics are fine-grained alternatives to extension-based and labelling-based semantics. They ascribe a dialectical strength to (components of) arguments sanctioning their degree of acceptability. Several gradual semantics have been studied for abstract, bipolar and quantitative bipolar argumentation frameworks (QBAFs), as well as, to a lesser extent, for some forms of structured argumentation. However, this has not been the case for assumption-based argumentation (ABA), despite it being a popular form of structured argumentation with several applications where gradual semantics could be useful. In this paper, we fill this gap and propose a family of novel gradual semantics for equipping assumptions, which are the core components in ABA frameworks, with dialectical strengths. To do so, we use bipolar set-based argumentation frameworks as an abstraction of (potentially non-flat) ABA frameworks and generalise state-of-the-art modular gradual semantics for QBAFs. We show that our gradual ABA semantics satisfy suitable adaptations of desirable properties of gradual QBAF semantics, such as balance and monotonicity. We also explore an argument-based approach that leverages established QBAF modular semantics directly, and use it as baseline. Finally, we conduct experiments with synthetic ABA frameworks to compare our gradual ABA semantics with its argument-based counterpart and assess convergence.

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KR Conference 2024 Conference Paper

Argumentative Causal Discovery

  • Fabrizio Russo
  • Anna Rapberger
  • Francesca Toni

Causal discovery amounts to unearthing causal relationships amongst features in data. It is a crucial companion to causal inference, necessary to build scientific knowledge without resorting to expensive or impossible randomised control trials. In this paper, we explore how reasoning with symbolic representations can support causal discovery. Specifically, we deploy assumption-based argumentation (ABA), a well-established and powerful knowledge representation formalism, in combination with causality theories, to learn graphs which reflect causal dependencies in the data. We prove that our method exhibits desirable properties, notably that, under natural conditions, it can retrieve ground-truth causal graphs. We also conduct experiments with an implementation of our method in answer set programming (ASP) on four datasets from standard benchmarks in causal discovery, showing that our method compares well against established baselines.

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KR Conference 2024 Conference Paper

Contestable AI Needs Computational Argumentation

  • Francesco Leofante
  • Hamed Ayoobi
  • Adam Dejl
  • Gabriel Freedman
  • Deniz Gorur
  • Junqi Jiang
  • Guilherme Paulino-Passos
  • Antonio Rago

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 guidelines (e. g. by the OECD) and regulation of automated decision-making (e. g. GDPR). In this position paper we explore how contestability can be achieved computationally in and for AI. We argue that contestable AI requires dynamic (human-machine and/or machine-machine) explainability and decision-making processes, whereby machines can 1. interact with humans and/or other machines to progressively explain their outputs and/or their reasoning as well as assess grounds for contestation provided by these humans and/or other machines, and 2. revise their decision-making processes to redress any issues successfully raised during contestation. Given that much of the current AI landscape is tailored to static AIs, the need to accommodate contestability will require a radical rethinking, that, we argue, computational argumentation is ideally suited to support.

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IJCAI Conference 2023 Conference Paper

Argumentation for Interactive Causal Discovery

  • Fabrizio Russo

Causal reasoning reflects how humans perceive events in the world and establish relationships among them, identifying some as causes and others as effects. Causal discovery is about agreeing on these relationships and drawing them as a causal graph. Argumentation is the way humans reason systematically about an idea: the medium we use to exchange opinions, to get to know and trust each other and possibly agree on controversial matters. Developing AI which can argue with humans about causality would allow us to understand and validate the analysis of the AI and would allow the AI to bring evidence for or against humans' prior knowledge. This is the goal of this project: to develop a novel scientific paradigm of interactive causal discovery and train AI to recognise causes and effects by debating, with humans, the results of different statistical methods

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FLAP Journal 2023 Journal Article

Explaining Classifiers' Outputs with Causal Models and Argumentation.

  • Antonio Rago
  • Fabrizio Russo
  • Emanuele Albini
  • Francesca Toni
  • Pietro Baroni

We introduce a conceptualisation for generating argumentation frameworks (AFs) from causal models for the purpose of forging explanations for models’ outputs. The conceptualisation is based on reinterpreting properties of semantics of AFs as explanation moulds, which are means for characterising argumentative relations. We demonstrate our methodology by reinterpreting the property of bi-variate reinforcement in bipolar AFs, showing how the extracted bipolar AFs may be used as relation-based explanations for the outputs of causal models. We then evaluate our method empirically when the causal models represent (Bayesian and neural network) machine learning models for classification. The results show advantages over a popular approach from the literature, both in highlighting specific relationships between feature and classification variables and in generating counterfactual explanations with respect to a commonly used metric.

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