LTLf Synthesis Under Unreliable Input

December 19, 2024 Β· Declared Dead Β· πŸ› AAAI Conference on Artificial Intelligence

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Authors Christian Hagemeier, Giuseppe de Giacomo, Moshe Y. Vardi arXiv ID 2412.14728 Category cs.AI: Artificial Intelligence Cross-listed cs.LO Citations 0 Venue AAAI Conference on Artificial Intelligence Last Checked 5 months ago
Abstract
We study the problem of realizing strategies for an LTLf goal specification while ensuring that at least an LTLf backup specification is satisfied in case of unreliability of certain input variables. We formally define the problem and characterize its worst-case complexity as 2EXPTIME-complete, like standard LTLf synthesis. Then we devise three different solution techniques: one based on direct automata manipulation, which is 2EXPTIME, one disregarding unreliable input variables by adopting a belief construction, which is 3EXPTIME, and one leveraging second-order quantified LTLf (QLTLf), which is 2EXPTIME and allows for a direct encoding into monadic second-order logic, which in turn is worst-case nonelementary. We prove their correctness and evaluate them against each other empirically. Interestingly, theoretical worst-case bounds do not translate into observed performance; the MSO technique performs best, followed by belief construction and direct automata manipulation. As a byproduct of our study, we provide a general synthesis procedure for arbitrary QLTLf specifications.
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