Learning-based symbolic assume-guarantee reasoning for Markov decision process by using interval Markov process

Learning-based symbolic assume-guarantee reasoning for Markov decision process by using interval... Many real-life critical systems are described with large models and exhibit both probabilistic and non-deterministic behaviour. Verification of such systems requires techniques to avoid the state space explosion problem. Symbolic model checking and compositional verification such as assume-guarantee reasoning are two promising techniques to overcome this barrier. In this paper, we propose a probabilistic symbolic compositional verification approach (PSCV) to verify probabilistic systems where each component is a Markov decision process (MDP). PSCV starts by encoding implicitly the system components using compact data structures. To establish the symbolic compositional verification process, we propose a sound and com- plete symbolic assume-guarantee reasoning rule. To attain completeness of the symbolic assume-guarantee reasoning rule, we propose to model assumptions using interval MDP. In addition, we give a symbolic MTBDD-learning algorithm to gen- erate automatically the symbolic assumptions. Moreover, we propose to use causality to generate small counterexamples in order to refine the conjecture assumptions. Experimental results suggest promising outlooks for our probabilistic symbolic compositional approach. Keywords Probabilistic model checking · Compositional verification · Symbolic model checking · Assume-guarantee paradigm · Model learning 1 Introduction [7]. Probabilistic verification is a set of techniques for for- mal modelling and analysis of such systems. Probabilistic http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Innovations in Systems and Software Engineering Springer Journals

Learning-based symbolic assume-guarantee reasoning for Markov decision process by using interval Markov process

Loading next page...
 
/lp/springer_journal/learning-based-symbolic-assume-guarantee-reasoning-for-markov-decision-bgSSyD0Dvo
Publisher
Springer London
Copyright
Copyright © 2018 by Springer-Verlag London Ltd., part of Springer Nature
Subject
Computer Science; Software Engineering; Computing Methodologies; Computer Applications
ISSN
1614-5046
eISSN
1614-5054
D.O.I.
10.1007/s11334-018-0316-7
Publisher site
See Article on Publisher Site

Abstract

Many real-life critical systems are described with large models and exhibit both probabilistic and non-deterministic behaviour. Verification of such systems requires techniques to avoid the state space explosion problem. Symbolic model checking and compositional verification such as assume-guarantee reasoning are two promising techniques to overcome this barrier. In this paper, we propose a probabilistic symbolic compositional verification approach (PSCV) to verify probabilistic systems where each component is a Markov decision process (MDP). PSCV starts by encoding implicitly the system components using compact data structures. To establish the symbolic compositional verification process, we propose a sound and com- plete symbolic assume-guarantee reasoning rule. To attain completeness of the symbolic assume-guarantee reasoning rule, we propose to model assumptions using interval MDP. In addition, we give a symbolic MTBDD-learning algorithm to gen- erate automatically the symbolic assumptions. Moreover, we propose to use causality to generate small counterexamples in order to refine the conjecture assumptions. Experimental results suggest promising outlooks for our probabilistic symbolic compositional approach. Keywords Probabilistic model checking · Compositional verification · Symbolic model checking · Assume-guarantee paradigm · Model learning 1 Introduction [7]. Probabilistic verification is a set of techniques for for- mal modelling and analysis of such systems. Probabilistic

Journal

Innovations in Systems and Software EngineeringSpringer Journals

Published: Jun 1, 2018

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off