Publication detail

Shepherding Hordes of Markov Chains

ČEŠKA, M. JANSEN, N. JUNGES, S. KATOEN, J.

Original Title

Shepherding Hordes of Markov Chains

Type

conference paper

Language

English

Original Abstract

This paper considers large families of Markov chains (MCs) that are defined over a set of parameters with finite discrete domains. Such families occur in software product lines, planning under partial observability, and sketching of probabilistic programs. Simple questions, like does at least one family member satisfy a property?, are NP-hard. We tackle two problems: distinguish family members that satisfy a given quantitative property from those that do not, and determine a family member that satisfies the property optimally, i.e., with the highest probability or reward. We show that combining two well-known techniques, MDP model checking and abstraction refinement, mitigates the computational complexity. Experiments on a broad set of benchmarks show that in many situations, our approach is able to handle families of millions of MCs, providing superior scalability compared to existing solutions.

Keywords

parametric Markov chains, synthesis from specification, Markov Decision Processes, abstraction refinement

Authors

ČEŠKA, M.; JANSEN, N.; JUNGES, S.; KATOEN, J.

Released

17. 4. 2019

Publisher

Springer International Publishing

Location

Praha

ISBN

978-3-030-17464-4

Book

Proceedings of 25th International Conference on Tools and Algorithms for the Construction and Analysis of Systems

Edition

Lecture Notes in Computer Science

Pages from

172

Pages to

190

Pages count

19

URL

https://link.springer.com/chapter/10.1007/978-3-030-17465-1_10

Full text in the Digital Library

http://hdl.handle.net/11012/195257

BibTex

@inproceedings{BUT156852,
  author="ČEŠKA, M. and JANSEN, N. and JUNGES, S. and KATOEN, J.",
  title="Shepherding Hordes of Markov Chains",
  booktitle="Proceedings of 25th International Conference on Tools and Algorithms for the Construction and Analysis of Systems",
  year="2019",
  series="Lecture Notes in Computer Science",
  volume="11428",
  pages="172--190",
  publisher="Springer International Publishing",
  address="Praha",
  doi="10.1007/978-3-030-17465-1\{_}10",
  isbn="978-3-030-17464-4",
  url="https://link.springer.com/chapter/10.1007/978-3-030-17465-1_10"
}