NDLI: Temporal logic inference for classification and prediction from data

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Temporal logic inference for classification and prediction from data

Content Provider	ACM Digital Library
Author	Belta, Calin Jones, Austin Kong, Zhaodan Aydin Gol, Ebru Medina Ayala, Ana
Abstract	This paper presents an inference algorithm that can discover temporal logic properties of a system from data. Our algorithm operates on finite time system trajectories that are labeled according to whether or not they demonstrate some desirable system properties (e.g. "the car successfully stops before hitting an obstruction"). A temporal logic formula that can discriminate between the desirable behaviors and the undesirable ones is constructed. The formulae also indicate possible causes for each set of behaviors (e.g. "If the speed of the car is greater than 15 m/s within 0.5s of brake application, the obstruction will be struck") which can be used to tune designs or to perform on-line monitoring to ensure the desired behavior. We introduce reactive parameter signal temporal logic (rPSTL), a fragment of parameter signal temporal logic (PSTL) that is expressive enough to capture causal, spatial, and temporal relationships in data. We define a partial order over the set of rPSTL formulae that is based on language inclusion. This order enables a directed search over this set, i.e. given a candidate rPSTL formula that does not adequately match the observed data, we can automatically construct a formula that will fit the data at least as well. Two case studies, one involving a cattle herding scenario and one involving a stochastic hybrid gene circuit model, are presented to illustrate our approach.
Starting Page	273
Ending Page	282
Page Count	10
File Format	PDF
ISBN	9781450327329
DOI	10.1145/2562059.2562146
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-04-15
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Directed acyclic graph Gene network Logic inference Parametric signal temporal logic
Content Type	Text
Resource Type	Article

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