most citedQuantifying over Optimum Answer Sets

3 citations · 3 across the 5 of their papers we have counts for

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5 papers

cs.AI2026

2-ASP(Q) programs with weak constraints: Complexity and efficient implementation

Andrea Cuteri, Giuseppe Mazzotta, Francesco Ricca

ASP(Q) extends Answer Set Programming (ASP) with Quantifiers over answer sets. In this paper we focus on the class of ASP(Q) programs with two quantifiers and weak constraints, den…

cs.LO2026

Using ASP(Q) to Handle Inconsistent Prioritized Data

Meghyn Bienvenu, Camille Bourgaux, Robin Jean +1

We explore the use of answer set programming (ASP) and its extension with quantifiers, ASP(Q), for inconsistency-tolerant querying of prioritized data, where a priority relation be…

cs.AI2024

Enumerating Minimal Unsatisfiable Cores of LTLf formulas

Antonio Ielo, Giuseppe Mazzotta, Rafael Peñaloza +1

Linear Temporal Logic over finite traces () is a widely used formalism with applications in AI, process mining, model checking, and more. The primary reasoning task f…

cs.AI2024★ 3 cited

Quantifying over Optimum Answer Sets

Giuseppe Mazzotta, Francesco Ricca, Mirek Truszczynski

Answer Set Programming with Quantifiers (ASP(Q)) has been introduced to provide a natural extension of ASP modeling to problems in the polynomial hierarchy (PH). However, ASP(Q) la…

cs.SE2024

Unit Testing in ASP Revisited: Language and Test-Driven Development Environment

Giovanni Amendola, Tobias Berei, Giuseppe Mazzotta +1

Unit testing frameworks are nowadays considered a best practice, included in almost all modern software development processes, to achieve rapid development of correct specification…