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Domenico Mandaglio

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4 papers

2 author rows

AAAI Conference 2026 Conference Paper

Conditional Probabilistic Bipolar Argumentation Framework: Explanations, Complexity and Approximation

Gianvincenzo Alfano
Sergio Greco
Domenico Mandaglio
Francesco Parisi
Irina Trubitsyna

Recently, there has been an increasing interest in extending Dung's framework with probability theory, leading to the Probabilistic Argumentation Framework (PAF), and with supports in addition to attacks, leading to the Bipolar Argumentation Framework (BAF). In this paper, we introduce the Conditional Probabilistic Bipolar Argumentation Framework (CPBAF), which extends Probabilistic and Bipolar AF by allowing conditional probabilities on arguments, attacks, and on (possibly cyclic) supports. In this setting, we address the problem of computing the probability that a given argument is accepted. This is carried out by introducing the concept of probabilistic explanation for a given (probabilistic) extension. We show that the complexity of the problem is FP^#P-hard and propose polynomial approximation algorithms with bounded additive error for CPBAF where cycles with an odd number of attacks are forbidden.

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AAAI Conference 2025 Conference Paper

Even-if Explanations: Formal Foundations, Priorities and Complexity

Gianvincenzo Alfano
Sergio Greco
Domenico Mandaglio
Francesco Parisi
Reza Shahbazian
Irina Trubitsyna

Explainable AI has received significant attention in recent years. Machine learning models often operate as black boxes, lacking explainability and transparency while supporting decision-making processes. Local post-hoc explainability queries attempt to answer why individual inputs are classified in a certain way by a given model. While there has been important work on counterfactual explanations, less attention has been devoted to semifactual ones. In this paper, we focus on local post-hoc explainability queries within the semifactual `even-if' thinking and their computational complexity among different classes of models, and show that both linear and tree-based models are strictly more interpretable than neural networks. After this, we introduce a preference-based framework enabling users to personalize explanations based on their preferences, both in the case of semifactuals and counterfactuals, enhancing interpretability and user-centricity. Finally, we explore the complexity of several interpretability problems in the proposed preference-based framework and provide algorithms for polynomial cases.

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AIJ Journal 2024 Journal Article

Abstract argumentation frameworks with strong and weak constraints

Gianvincenzo Alfano
Sergio Greco
Domenico Mandaglio
Francesco Parisi
Irina Trubitsyna

Dealing with controversial information is an important issue in several application contexts. Formal argumentation enables reasoning on arguments for and against a claim to decide on an outcome. Dung's abstract Argumentation Framework (AF) has emerged as a central formalism in argument-based reasoning. Key aspects of the success and popularity of Dung's framework include its simplicity and expressiveness. Integrity constraints help to express domain knowledge in a compact and natural way, thus keeping easy the modeling task even for problems that otherwise would be hard to encode within an AF. In this paper, we first explore two intuitive semantics based on Kleene and Lukasiewicz logics, respectively, for AF augmented with (strong) constraints—the resulting argumentation framework is called Constrained AF (CAF). Then, we propose a new argumentation framework called Weak constrained AF (WAF) that enhances CAF with weak constraints. Intuitively, these constraints can be used to find “optimal” solutions to problems defined through CAF. We provide a detailed complexity analysis of CAF and WAF, showing that strong constraints do not increase the expressive power of AF in most cases, while weak constraints systematically increase the expressive power of CAF (and AF) under several well-known argumentation semantics.

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ECAI Conference 2023 Conference Paper

Complexity of Verification and Existence Problems in Epistemic Argumentation Framework

Gianvincenzo Alfano
Sergio Greco
Domenico Mandaglio
Francesco Parisi
Irina Trubitsyna

Dung’s Argumentation Framework (AF) has been extended in several directions. An interesting extension, among others, is the Epistemic AF (EAF) which allows representing the agent’s belief by means of epistemic constraints. In particular, an epistemic constraint is a propositional formula over labeled arguments (e. g. in(a), out(c)) extended with the modal operators K and M that intuitively state that the agent believes that a given formula is certainly or possibly true, respectively. In this paper, focusing on EAF, we investigate the complexity of the possible and necessary variants of three canonical problems in abstract argumentation: verification, existence, and non-empty existence. Moreover, we explore the relationship between EAF and incomplete AF (iAF), an extension of AF where arguments and attacks may be uncertain. Our complexity analysis shows that the verification problem in iAF can be naturally reduced to the verification in EAF, while it turns out that a similar result cannot hold for the necessary (non-empty) existence problem.

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