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Data-driven statistical learning of temporal logic properties

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

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
StatePublished - 2014

Publication series

NameLecture Notes in Computer Science
Volume8711

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.

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