VLDB 2026 Research / reviewers in the wild / expert
Giuliano Rosella
dblp:345/9467
· DBLP profile ↗
4ranked-venue papers
2as first author
4since 2021 · last 2025
0000-0002-3148-6125ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 3 · 1 first-author · 3 since 2021Theory of computation · 2 · 1 first-author · 2 since 2021
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | On Measuring the Possibility of Selection Function-Based Conditionals, General Updates, and Qualitative Capacities
Tommaso Flaminio, Lluís Godo, Giuliano Rosella |
ECSQARU | 3 |
| 2024 | Possibility of Conditionals and Conditional Possibilities: From the Triviality Result to Possibilistic ImagingabstractLewis-Gärdenfors imaging is an updating procedure for probability functions that generalizes Bayesian conditionalization, allowing to approach the probability of conditionals and counterfactual formulas without incurring in Lewis well-known triviality result. Precisely, while the probability of a so called Stalnaker conditional (as formalizable in Lewis logic C2) was proved to be an imaged probability by Lewis in his celebrated paper from 1976, a variant of Gärdenfors generalized imaging (proposed by Dubois and Prade in 1994) has been recently proved to characterize the probability of Lewis counterfactuals, the latter refer to those conditionals of Lewis’s logic C1. The present contribution extends the analysis on Lewis’s triviality result, imaging and generalized imaging to cope with possibility and necessity measures. In particular, after showing that the triviality result also holds in the possibilistic framework, we introduce a way to define the possibility measure of conditional and counterfactual formula. Then we prove that the possibilistic version of Lewis-Gärdenfors imaging (that is inspired by a definition given again by Dubois and Prade in 1994) actually characterizes, as in the aforementioned cases, the possibility of Stalnaker conditionals and Lewis counterfactuals. Furthermore, we show that possibilistic imaging can also be described within the setting of Boolean algebras of conditionals and Lewis algebras. These are algebraic models for conditional and counterfactual formulas recently introduced by two of the present authors. On these structures one can (canonically) define a notion of possibility measure that turns out to be the conditional possibility and imaged possibility mentioned above, respectively, and hence it represents the possibility of conditional and counterfactual formulas. Tommaso Flaminio, Lluís Godo, Giuliano Rosella |
KR | 3 |
| 2024 | Causal modeling semantics for counterfactuals with disjunctive antecedents
Giuliano Rosella, Jan Sprenger |
Ann. Pure Appl. Log. | 1 |
| 2023 | Counterfactuals as modal conditionals, and their probabilityabstractIn this paper we propose a semantic analysis of Lewis' counterfactuals. By exploiting the structural properties of the recently introduced boolean algebras of conditionals, we show that counterfactuals can be expressed as formal combinations of a conditional object and a normal necessity modal operator. Specifically, we introduce a class of algebras that serve as modal expansions of boolean algebras of conditionals, together with their dual relational structures. Moreover, we show that Lewis' semantics based on sphere models can be reconstructed in this framework. As a consequence, we establish the soundness and completeness of a slightly stronger variant of Lewis' logic for counterfactuals with respect to our algebraic models. In the second part of the paper, we present a novel approach to the probability of counterfactuals showing that it aligns with the uncertainty degree assigned by a belief function, as per Dempster-Shafer theory, to its associated conditional formula. Furthermore, we characterize the probability of a counterfactual in terms of Gärdenfors' imaging rule for the probabilistic update. Giuliano Rosella, Tommaso Flaminio, Stefano Bonzio |
Artif. Intell. | 1 |