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Input Attribution for Statistical Model Checking Using Logistic Regression

  • Jeffery P. HansenEmail author
  • Sagar Chaki
  • Scott Hissam
  • James Edmondson
  • Gabriel A. Moreno
  • David Kyle
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10012)

Abstract

We describe an approach to Statistical Model Checking (SMC) that produces not only an estimate of the probability that specified properties (a.k.a. predicates) are satisfied, but also an “input attribution” for those predicates. We use logistic regression to generate the input attribution as a set of linear and non-linear functions of the inputs that explain conditions under which a predicate is satisfied. These functions provide quantitative insight into factors that influence the predicate outcome. We have implemented our approach on a distributed SMC infrastructure, demeter, that uses Linux Docker containers to isolate simulations (a.k.a. trials) from each other. Currently, demeter is deployed on six 20-core blade servers, and can perform tens of thousands of trials in a few hours. We demonstrate our approach on examples involving robotic agents interacting in a simulated physical environment. Our approach synthesizes input attributions that are both meaningful to the investigator and have predictive value on the predicate outcomes.

Keywords

Logistic Regression Random Input Initial Distance Polynomial Term Statistical Model Check 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Jeffery P. Hansen
    • 1
    Email author
  • Sagar Chaki
    • 1
  • Scott Hissam
    • 1
  • James Edmondson
    • 1
  • Gabriel A. Moreno
    • 1
  • David Kyle
    • 1
  1. 1.Carnegie Mellon UniversityPittsburghUSA

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