Year: 2025
Type: preprint
Abstract: Learning formulas in Linear Temporal Logic (LTLf) from finite traces is a fundamental research problem which has found applications in artificial intelligence, software engineering, programming languages, formal methods, control of cyber-physical systems, and robotics. We implement a new CPU tool called Bolt improving over the state of the art by learning formulas more than 100x faster over 70% of the benchmarks, with smaller or equal formulas in 98% of the cases. Our key insight is to leverage a problem called Boolean Set Cover as a subroutine to combine existing formulas using Boolean connectives. Thanks to the Boolean Set Cover component, our approach offers a novel trade-off between efficiency and formula size.
Source: arXiv (Cornell University)
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7084165309
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