We study best-effort synthesis under environment assumptions specified in LTL, and show that this problem has exactly the same computational complexity of standard LTL synthesis: 2EXPTIME-complete. We provide optimal algorithms for computing best-effort strategies, both in the case of LTL over infinite traces and LTL over finite traces (i.e., LTLf). The latter are particularly well suited for implementation.

Best-Effort Synthesis: Doing Your Best Is Not Harder Than Giving Up / Aminof, Benjamin; DE GIACOMO, Giuseppe; Rubin, Sasha. - (2021). ((Intervento presentato al convegno Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021 tenutosi a Montreal, Canada [10.24963/ijcai.2021/243].

Best-Effort Synthesis: Doing Your Best Is Not Harder Than Giving Up

Giuseppe De Giacomo;
2021

Abstract

We study best-effort synthesis under environment assumptions specified in LTL, and show that this problem has exactly the same computational complexity of standard LTL synthesis: 2EXPTIME-complete. We provide optimal algorithms for computing best-effort strategies, both in the case of LTL over infinite traces and LTL over finite traces (i.e., LTLf). The latter are particularly well suited for implementation.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1575268
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