Parameter Synthesis for Parametric Probabilistic Dynamical Systems and Prefix-Independent Specifications - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2022

Parameter Synthesis for Parametric Probabilistic Dynamical Systems and Prefix-Independent Specifications

Abstract

We consider the model-checking problem for parametric probabilistic dynamical systems, formalised as Markov chains with parametric transition functions, analysed under the distribution-transformer semantics (in which a Markov chain induces a sequence of distributions over states). We examine the problem of synthesising the set of parameter valuations of a parametric Markov chain such that the orbits of induced state distributions satisfy a prefix-independent ω-regular property. Our main result establishes that in all non-degenerate instances, the feasible set of parameters is (up to a null set) semialgebraic, and can moreover be computed (in polynomial time assuming that the ambient dimension, corresponding to the number of states of the Markov chain, is fixed).
Fichier principal
Vignette du fichier
parametric-synthesis22.pdf (880.65 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03789856 , version 1 (27-09-2022)

Identifiers

Cite

Christel Baier, Florian Funke, Simon Jantsch, Toghrul Karimov, Engel Lefaucheux, et al.. Parameter Synthesis for Parametric Probabilistic Dynamical Systems and Prefix-Independent Specifications. 33rd International Conference on Concurrency Theory (CONCUR 2022), Sep 2022, Varsovie, Poland. ⟨10.4230/LIPIcs.CONCUR.2022.10⟩. ⟨hal-03789856⟩
67 View
44 Download

Altmetric

Share

Gmail Facebook X LinkedIn More