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Towards ILP-based LTLf passive learning

Year: 2025

Type: article

Abstract: Inferring linear temporal logic over finite traces ($\text{LTL}_{\text{f}}$) formulas from a set of example traces, known as passive learning, presents significant challenges due to its combinatorial nature. In this paper, we introduce a novel approach to $\text{LTL}_{\text{f}}$ passive learning based on inductive logic programming (ILP), leveraging the inductive learning of answer set programs framework. Our ILP-based method effectively exploits the set of example traces to guide the learning process, and experimental results demonstrate that it o ffers a more efficient solution compared to traditional techniques based on propositional satisfiability.

Source: Journal of Logic and Computation

DOI: 10.1093/logcom/exaf069

Authors: Antonio Ielo, Mark Law, Valeria Fionda, Francesco Ricca, Giuseppe De Giacomo, Alessandra Russo

Institutions: University of Calabria, Imperial College London, University of Oxford

Language: English

Topic: Logic, Reasoning, and Knowledge

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $4,533

OpenAlex ID: W7125974887

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Cites: 27

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