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Maximum Realizability for Linear Temporal Logic Specifications

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Automated Technology for Verification and Analysis (ATVA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11138))

Abstract

Automatic synthesis from linear temporal logic (LTL) specifications is widely used in robotic motion planning and control of autonomous systems. A common specification pattern in such applications consists of an LTL formula describing the requirements on the behaviour of the system, together with a set of additional desirable properties. We study the synthesis problem in settings where the overall specification is unrealizable, more precisely, when some of the desirable properties have to be (temporarily) violated in order to satisfy the system’s objective. We provide a quantitative semantics of sets of safety specifications, and use it to formalize the “best-effort” satisfaction of such soft specifications while satisfying the hard LTL specification. We propose an algorithm for synthesizing implementations that are optimal with respect to this quantitative semantics. Our method builds upon the idea of bounded synthesis, and we develop a MaxSAT encoding which allows for maximizing the quantitative satisfaction of the soft specifications. We evaluate our algorithm on scenarios from robotics and power distribution networks.

R. Dimitrova—This work was done while the author was at The University of Texas at Austin.

This work was supported in part by AFRL grants UTC 17-S8401-10-C1 and FA8650-15-C-2546, and ONR grant N000141613165.

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Notes

  1. 1.

    The code is available at https://github.com/MahsaGhasemi/max-realizability.

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Correspondence to Rayna Dimitrova .

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Dimitrova, R., Ghasemi, M., Topcu, U. (2018). Maximum Realizability for Linear Temporal Logic Specifications. In: Lahiri, S., Wang, C. (eds) Automated Technology for Verification and Analysis. ATVA 2018. Lecture Notes in Computer Science(), vol 11138. Springer, Cham. https://doi.org/10.1007/978-3-030-01090-4_27

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  • DOI: https://doi.org/10.1007/978-3-030-01090-4_27

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