Two-layered falsification of hybrid systems guided by Monte Carlo tree search

Zhenya Zhang, Gidon Ernst, Sean Sedwards, Paolo Arcaini, Ichiro Hasuo

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)

Abstract

Few real-world hybrid systems are amenable to formal verification, due to their complexity and black box components. Optimization-based falsification- A methodology of search-based testing that employs stochastic optimization-is thus attracting attention as an alternative quality assurance method. Inspired by the recent work that advocates coverage and exploration in falsification, we introduce a two-layered optimization framework that uses Monte Carlo tree search (MCTS), a popular machine learning technique with solid mathematical and empirical foundations (e.g., in computer Go). MCTS is used in the upper layer of our framework; it guides the lower layer of local hill-climbing optimization, thus balancing exploration and exploitation in a disciplined manner. We demonstrate the proposed framework through experiments with benchmarks from the automotive domain.

Original languageEnglish
Article number8418450
Pages (from-to)2894-2905
Number of pages12
JournalIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Volume37
Issue number11
DOIs
Publication statusPublished - Nov 2018
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering

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