Electronic Proceedings in Theoretical Computer Science (Jul 2017)

SEA-PARAM: Exploring Schedulers in Parametric MDPs

  • Sebastian Arming,
  • Ezio Bartocci,
  • Ana Sokolova

DOI
https://doi.org/10.4204/EPTCS.250.3
Journal volume & issue
Vol. 250, no. Proc. QAPL 2017
pp. 25 – 38

Abstract

Read online

We study parametric Markov decision processes (PMDPs) and their reachability probabilities "independent" of the parameters. Different to existing work on parameter synthesis (implemented in the tools PARAM and PRISM), our main focus is on describing different types of optimal deterministic memoryless schedulers for the whole parameter range. We implement a simple prototype tool SEA-PARAM that computes these optimal schedulers and show experimental results.