EPR-based k-induction with Counterexample Guided Abstraction Refinement

14 pages•Published: December 18, 2015

Zurab Khasidashvili, Konstantin Korovin and Dmitry Tsarkov

Abstract

In recent years it was proposed to encode bounded model checking (BMC) into the effectively propositional fragment of first-order logic (EPR). The EPR fragment can provide for a succinct representation of the problem and facilitate reasoning at a higher level.
In this paper we present an extension of the EPR-based bounded model checking
with k-induction which can be used to prove safety properties of systems over
unbounded runs. We present a novel abstraction-refinement approach based on
unsatisfiable cores and models (UCM) for BMC and k-induction in the EPR setting.
We have implemented UCM refinements for EPR-based BMC and k-induction in a first-order automated theorem prover iProver. We also extended iProver with the AIGER format and evaluated it over the HWMCC'14 competition benchmarks. The experimental results are encouraging. We show that a number of AIG problems can be verified until deeper bounds with the EPR-based model checking.

Keyphrases: abstraction refinement, bounded model checking, epr fragment, first order logic, k induction

In: Georg Gottlob, Geoff Sutcliffe and Andrei Voronkov (editors). GCAI 2015. Global Conference on Artificial Intelligence, vol 36, pages 137-150.

Links:	https://easychair.org/publications/paper/MF
	https://doi.org/10.29007/scv7

BibTeX entry

@inproceedings{GCAI2015:EPR_based_k_induction,
  author    = {Zurab Khasidashvili and Konstantin Korovin and Dmitry Tsarkov},
  title     = {EPR-based k-induction with Counterexample Guided Abstraction Refinement},
  booktitle = {GCAI 2015. Global Conference on Artificial Intelligence},
  editor    = {Georg Gottlob and Geoff Sutcliffe and Andrei Voronkov},
  series    = {EPiC Series in Computing},
  volume    = {36},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/MF},
  doi       = {10.29007/scv7},
  pages     = {137-150},
  year      = {2015}}

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