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Linear temporal logic (LTL) is a specification language for finite sequences(called traces) widely used in program verification, motion planning inrobotics, process mining, and many other areas. We consider the problem oflearning LTL formulas for classifying traces; despite a growing interest of theresearch community, existing solutions suffer from two limitations: they do notscale beyond small formulas, and they may exhaust computational resourceswithout returning any result. We introduce a new algorithm addressing bothissues: our algorithm is able to construct formulas an order of magnitudelarger than previous methods, and it is anytime, meaning that it in most casessuccessfully outputs a formula, albeit possibly not of minimal size. Weevaluate the performances of our algorithm using an open source implementationagainst publicly available benchmarks.

Scalable Anytime Algorithms for Learning Fragments of Linear Temporal Logic