LTLf Synthesis Under Unreliable Input
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Abstract (arXiv)
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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References · 8
- 2022Partially Observable Markov Decision Processes in Robotics: A Surveycited by 81
- 2017Symbolic LTLf Synthesiscited by 21
- 2017Probabilistic Motion Planning under Temporal Tasks and Soft Constraintscited by 9
- 2020What to Do When You Can't Do It All: Temporal Logic Planning with Soft Temporal Logic Constraintscited by 7
- 2019First-Order vs. Second-Order Encodings for LTLf-to-Automata Translationcited by 5
- 2019Unpredictable Planning Under Partial Observabilitycited by 2
- 2020LTLf Synthesis under Partial Observability: From Theory to Practicecited by 2
- 2024The Trembling-Hand Problem for LTLf Planningcited by 2
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