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Antonio Ielo

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4 papers

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AAAI Conference 2026 Conference Paper

Computing Syntax Tree-based Minimal Unsatisfiable Cores of LTLf Formulas

Valeria Fionda
Antonio Ielo
Francesco Ricca

Linear Temporal Logic on Finite Traces (LTLf) is a popular logic to express declarative specifications in Artificial Intelligence (AI). The recent call for explainable AI tools has made relevant the problem of computing efficiently minimal unsatisfiable cores (MUCs) and minimal correction sets (MCSes) of LTLf formulas. Recent work has focused on the extraction of MUCs on formulas in conjunctive form. In this paper, we present a method that operates on arbitrary formulas and computes a more refined notion of MUCs, as introduced by Schuppan, along with the corresponding notion of MCSes. Experiments show that our system, based on Answer Set Programming, outperforms available tools.

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AAAI Conference 2026 Conference Paper

Enumerating Minimal Unsatisfiable Cores of LTLf Formulae

Antonio Ielo
Giuseppe Mazzotta
Rafael Peñaloza
Francesco Ricca

Linear Temporal Logic over finite traces (LTLf) is a widely used formalism with applications in AI, process mining, model checking, and more. The primary reasoning task for LTLf is satisfiability checking; yet, the recent focus on explainable AI has increased interest in analyzing inconsistent formulae, making the enumeration of minimal explanations for unsatisfiability a relevant task also for LTLf. We introduce a novel technique for enumerating minimal unsatisfiable cores (MUCs) of an LTLf specification. The main idea is to encode an LTLf formula into an Answer Set Programming (ASP) specification, such that the minimal unsatisfiable subsets (MUSes) of the ASP program directly correspond to the MUCs of the original LTLf specification. Leveraging recent advancements in ASP solving yields an MUC enumerator achieving good performance in experiments conducted on established benchmarks from the literature.

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IJCAI Conference 2025 Conference Paper

Are Large Language Models Fluent in Declarative Process Mining?

Valeria Fionda
Antonio Ielo
Francesco Ricca

Recent advancements in AI have made LLMs valuable tools for automating the interpretation of textual descriptions of business processes and for converting formal process specifications into natural language. However, there are no practical methodologies or systematic assessments to ensure these automatic translations are faithful. This paper proposes a novel approach, based on an auxiliary bidirectional translation task, to assess LLMs performance quantitatively; also, it also empirically evaluates the performance of state-of-the-art LLMs for bidirectional translations between natural language and declarative formal process specifications. The results reveal substantial variability in performance among the LLMs, highlighting the importance of LLM selection and confirming the need for a robust method for assessing LLMs' outputs.

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KR Conference 2023 Conference Paper

Logic-based Composition of Business Process Models

Valeria Fionda
Antonio Ielo
Francesco Ricca

Process Mining is a family of techniques that exploit data collected from process execution to analyze and improve process efficiency, quality, and security. Over the years, many modeling languages have been proposed for process model specification, with different expressiveness, features, and computational properties. We propose a new logic-based declarative formalism, called Constraint Formulae, to compose process specifications, expressed in heterogeneous process modeling languages, without altering their original semantics. We formalize common process mining tasks for Constraint Formulae, study their computational properties, and provide an implementation in Answer Set Programming.

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