Title
Symbolic counterexample generation for large discrete-time Markov chains.
Abstract
This paper presents several symbolic counterexample generation algorithms for discrete-time Markov chains (DTMCs) violating a PCTL formula. A counterexample is (a symbolic representation of) a sub-DTMC that is incrementally generated. The crux to this incremental approach is the symbolic generation of paths that belong to the counterexample. We consider two approaches. First, we extend bounded model checking and develop a simple heuristic to generate highly probable paths first. We then complement the SAT-based approach by a fully (multi-terminal) BDD-based technique. All symbolic approaches are implemented, and our experimental results show a substantially better scalability than existing explicit techniques. In particular, our BDD-based approach using a method called fragment search allows for counterexample generation for DTMCs with billions of states (up to 1015).
Year
DOI
Venue
2014
10.1016/j.scico.2014.02.001
Science of Computer Programming
Keywords
Field
DocType
Markov chain,Counterexample,Model checking,Binary decision diagram
Heuristic,Model checking,Computer science,Markov chain,Algorithm,Binary decision diagram,Theoretical computer science,Discrete time and continuous time,Counterexample,Symbolic trajectory evaluation,Bounded function
Journal
Volume
ISSN
Citations 
91
0167-6423
7
PageRank 
References 
Authors
0.44
39
7
Name
Order
Citations
PageRank
Nils Jansen128427.77
Ralf Wimmer240734.28
Erika Ábrahám383063.17
Barna Zajzon4110.84
Joost-Pieter Katoen54444289.65
Bernd Becker685573.74
Johann Schuster7283.69