LTLf Synthesis Under Unreliable Input

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.

Authors

Publication Details

Journal
Proceedings of the AAAI Conference on Artificial Intelligence
Published
2025-04-11
DOI
https://doi.org/10.1609/aaai.v39i14.33640
Citations
1
Primary Topic
Advanced Control Systems Design
Type
article
Field-Weighted Citation Impact
2.16
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article

LTLf Synthesis Under Unreliable Input

Moshe Y. Vardi, Giuseppe De Giacomo, Christian Hagemeier
1 citations
Proceedings of the AAAI Conference on Artificial Intelligence
Advanced Control Systems Design
2.16
article

LTLf Synthesis Under Unreliable Input

Moshe Y. Vardi, Giuseppe De Giacomo, Christian Hagemeier
article en
1 citations

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.

Proceedings of the AAAI Conference on Artificial IntelligenceVol. 39(14)
Openalex Percentile: Top 16%
Advanced Control Systems Design
2.16
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LTLf Synthesis Under Unreliable Input — Moshe Y. Vardi, Giuseppe De Giacomo, et al. · Proceedings of the AAAI Conference on Artificial Intelligence (2025) | TGRS Research Map | TGRS