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Temporal logic robustness for general signal classes

Published: 16 April 2019 Publication History

Abstract

In multi-agent systems, robots transmit their planned trajectories to each other or to a central controller, and each receiver plans its own actions by maximizing a measure of mission satisfaction. For missions expressed in temporal logic, the robustness function plays the role of satisfaction measure. Currently, a Piece-Wise Linear (PWL) or piece-wise constant reconstruction is used at the receiver. This allows an efficient robustness computation algorithm - a.k.a. monitoring - but is not adaptive to the signal class of interest, and does not leverage the compression properties of more general representations. When communication capacity is at a premium, this is a serious bottleneck. In this paper we first show that the robustness computation is significantly affected by how the continuous-time signal is reconstructed from the received samples, which can mean the difference between a successful control and a crash. We show that monitoring general spline-based reconstructions yields a smaller robustness error, and that it can be done with the same time complexity as monitoring the simpler PWL reconstructions. Thus robustness computation can now be adapted to the signal class of interest. We further show that the monitoring error is tightly upper-bounded by the L signal reconstruction error. We present a (non-linear) L-based scheme which yields even lower monitoring error than the spline-based schemes (which have the advantage of being faster to compute), and illustrate all results on two case studies. As an application of these results, we show how time-frequency specifications can be efficiently monitored online.

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  • (2021)A few lessons learned in reinforcement learning for quadcopter attitude controlProceedings of the 24th International Conference on Hybrid Systems: Computation and Control10.1145/3447928.3456707(1-11)Online publication date: 19-May-2021
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cover image ACM Conferences
HSCC '19: Proceedings of the 22nd ACM International Conference on Hybrid Systems: Computation and Control
April 2019
299 pages
ISBN:9781450362825
DOI:10.1145/3302504
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 16 April 2019

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Author Tags

  1. monitoring
  2. multi-agent
  3. robustness
  4. sampling
  5. temporal logic

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  • (2024)Reinforcement learning with formal performance metrics for quadcopter attitude control under non-nominal contextsEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107090127:PAOnline publication date: 1-Feb-2024
  • (2024)Robust computation tree logicInnovations in Systems and Software Engineering10.1007/s11334-024-00552-7Online publication date: 20-Mar-2024
  • (2021)A few lessons learned in reinforcement learning for quadcopter attitude controlProceedings of the 24th International Conference on Hybrid Systems: Computation and Control10.1145/3447928.3456707(1-11)Online publication date: 19-May-2021
  • (2020)Compositional Probabilistic Analysis of Temporal Properties Over Stochastic DetectorsIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2020.301264339:11(3288-3299)Online publication date: Nov-2020
  • (2020)Specification, Synthesis and Validation of Strategies for Collaborative Embedded SystemsLeveraging Applications of Formal Methods, Verification and Validation: Applications10.1007/978-3-030-61467-6_23(366-385)Online publication date: 20-Oct-2020
  • (2020)Logical Signal Processing: A Fourier Analysis of Temporal LogicRuntime Verification10.1007/978-3-030-60508-7_20(359-382)Online publication date: 6-Oct-2020

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