Learning Spatio-Temporal Specifications for Dynamical Systems

Suhail Alsalehi, Erfan Aasi, Ron Weiss, Calin Belta
Proceedings of The 4th Annual Learning for Dynamics and Control Conference, PMLR 168:968-980, 2022.

Abstract

Learning dynamical systems properties from data provides valuable insights that help us understand such systems and mitigate undesired outcomes. We propose a framework for learning spatio-temporal (ST) properties as formal logic specifications from data. We introduce Support Vector Machine-Signal Temporal Logic (SVM-STL), an extension of Signal Temporal Logic (STL), capable of specifying spatial and temporal properties of a wide range of systems exhibiting time-varying spatial patterns. Our framework utilizes machine learning techniques to learn SVM-STL specifications from system executions given by sequences of spatial patterns. We present methods to deal with both labeled and unlabeled data. In addition, given system requirements in the form of SVM-STL specifications, we provide an approach for parameter synthesis to find parameters that maximize the satisfaction of such specifications. Our learning framework and parameter synthesis approach are showcased in an example of a reaction-diffusion system.

Cite this Paper

BibTeX
@InProceedings{pmlr-v168-alsalehi22a, title = {Learning Spatio-Temporal Specifications for Dynamical Systems}, author = {Alsalehi, Suhail and Aasi, Erfan and Weiss, Ron and Belta, Calin}, booktitle = {Proceedings of The 4th Annual Learning for Dynamics and Control Conference}, pages = {968--980}, year = {2022}, editor = {Firoozi, Roya and Mehr, Negar and Yel, Esen and Antonova, Rika and Bohg, Jeannette and Schwager, Mac and Kochenderfer, Mykel}, volume = {168}, series = {Proceedings of Machine Learning Research}, month = {23--24 Jun}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v168/alsalehi22a/alsalehi22a.pdf}, url = {https://proceedings.mlr.press/v168/alsalehi22a.html}, abstract = {Learning dynamical systems properties from data provides valuable insights that help us understand such systems and mitigate undesired outcomes. We propose a framework for learning spatio-temporal (ST) properties as formal logic specifications from data. We introduce Support Vector Machine-Signal Temporal Logic (SVM-STL), an extension of Signal Temporal Logic (STL), capable of specifying spatial and temporal properties of a wide range of systems exhibiting time-varying spatial patterns. Our framework utilizes machine learning techniques to learn SVM-STL specifications from system executions given by sequences of spatial patterns. We present methods to deal with both labeled and unlabeled data. In addition, given system requirements in the form of SVM-STL specifications, we provide an approach for parameter synthesis to find parameters that maximize the satisfaction of such specifications. Our learning framework and parameter synthesis approach are showcased in an example of a reaction-diffusion system.} }
Endnote
%0 Conference Paper %T Learning Spatio-Temporal Specifications for Dynamical Systems %A Suhail Alsalehi %A Erfan Aasi %A Ron Weiss %A Calin Belta %B Proceedings of The 4th Annual Learning for Dynamics and Control Conference %C Proceedings of Machine Learning Research %D 2022 %E Roya Firoozi %E Negar Mehr %E Esen Yel %E Rika Antonova %E Jeannette Bohg %E Mac Schwager %E Mykel Kochenderfer %F pmlr-v168-alsalehi22a %I PMLR %P 968--980 %U https://proceedings.mlr.press/v168/alsalehi22a.html %V 168 %X Learning dynamical systems properties from data provides valuable insights that help us understand such systems and mitigate undesired outcomes. We propose a framework for learning spatio-temporal (ST) properties as formal logic specifications from data. We introduce Support Vector Machine-Signal Temporal Logic (SVM-STL), an extension of Signal Temporal Logic (STL), capable of specifying spatial and temporal properties of a wide range of systems exhibiting time-varying spatial patterns. Our framework utilizes machine learning techniques to learn SVM-STL specifications from system executions given by sequences of spatial patterns. We present methods to deal with both labeled and unlabeled data. In addition, given system requirements in the form of SVM-STL specifications, we provide an approach for parameter synthesis to find parameters that maximize the satisfaction of such specifications. Our learning framework and parameter synthesis approach are showcased in an example of a reaction-diffusion system.
APA
Alsalehi, S., Aasi, E., Weiss, R. & Belta, C.. (2022). Learning Spatio-Temporal Specifications for Dynamical Systems. Proceedings of The 4th Annual Learning for Dynamics and Control Conference, in Proceedings of Machine Learning Research 168:968-980 Available from https://proceedings.mlr.press/v168/alsalehi22a.html.

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