Data-driven statistical learning of temporal logic properties

Ezio Bartocci, Luca Bortolussi, Guido Sanguinetti

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present a novel approach to learn logical formulae characterising the emergent behaviour of a dynamical system from system observations. At a high level, the approach starts by devising a data-driven statistical abstraction of the system. We then propose general optimisation strategies for selecting formulae with high satisfaction probability, either within a discrete set of formulae of bounded complexity, or a parametric family of formulae. We illustrate and apply the methodology on two real world case studies: characterising the dynamics of a biological circadian oscillator, and discriminating different types of cardiac malfunction from electro-cardiogram data. Our results demonstrate that this approach provides a statistically principled and generally usable tool to logically characterise dynamical systems in terms of temporal logic formulae.
Original languageEnglish
Title of host publicationFormal Modeling and Analysis of Timed Systems
Subtitle of host publication12th International Conference, FORMATS 2014, Florence, Italy, September 8-10, 2014. Proceedings
PublisherSpringer International Publishing
Pages23-37
Number of pages15
ISBN (Electronic)978-3-319-10512-3
ISBN (Print)978-3-319-10511-6
DOIs
Publication statusPublished - 2014

Publication series

NameLecture Notes in Computer Science
Volume8711

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