Scalable Anytime Algorithms for Learning Fragments of Linear Temporal Logic
This work addresses scalability and reliability issues in LTL learning for applications like program verification and robotics, representing an incremental improvement over prior methods.
The paper tackles the problem of learning Linear Temporal Logic (LTL) formulas for classifying traces, addressing scalability and resource exhaustion issues in existing methods. It introduces a new algorithm that constructs formulas an order of magnitude larger than previous methods and is anytime, successfully outputting formulas in most cases, though not necessarily minimal in size.
Linear temporal logic (LTL) 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 LTL formulas 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.