A scalable anytime algorithm for learning fragments of linear temporal logic

Ritam Raha

doi:10.1007/s10703-025-00489-y

A scalable anytime algorithm for learning fragments of linear temporal logic

2026-01-08

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DOI：10.1007/s10703-025-00489-y

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Linear temporal logic (LTLf) is a specification language for finite sequences (called traces) widely used in program verification, motion planning in robotics, process mining, and many other areas. We consider the problem of learning formulas in fragments of LTLf without the U-operator for classifying traces; despite a growing interest of the research community, existing solutions suffer from two limitations: they do not scale beyond small formulas, and they may exhaust computational resources without returning any result. We introduce a new algorithm addressing both issues: our algorithm is able to construct formulas an order of magnitude larger than previous methods, and it is anytime, meaning that it in most cases successfully outputs a formula, albeit possibly not of minimal size. We evaluate the performances of our algorithm using an open source implementation against publicly available benchmarks.

Keywords:

LTL Learning

Specification mining

Temporal logic learning

Logic synthesis

A scalable anytime algorithm for learning fragments of linear temporal logic

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