This paper deals with the notion of approximate probabilistic bisimulation (APB) relation for discrete-time labeled Markov Chains (LMC). In order to provide a quantified upper bound on a metric over probabilistic realizations for LMC, we exploit the structure and properties of the APB and leverage the mathematical framework of Markov set- Chains. Based on this bound, the article proves that the existence of an APB implies the preservation of robust PCTL formulae, which are formulae that allow being properly relaxed or strengthened, according to the underlying APB. This leads to a notion of robustness for probabilistic model checking. Copyright 2012 ACM.

Robust PCTL model checking

D'INNOCENZO, ALESSANDRO;
2012

Abstract

This paper deals with the notion of approximate probabilistic bisimulation (APB) relation for discrete-time labeled Markov Chains (LMC). In order to provide a quantified upper bound on a metric over probabilistic realizations for LMC, we exploit the structure and properties of the APB and leverage the mathematical framework of Markov set- Chains. Based on this bound, the article proves that the existence of an APB implies the preservation of robust PCTL formulae, which are formulae that allow being properly relaxed or strengthened, according to the underlying APB. This leads to a notion of robustness for probabilistic model checking. Copyright 2012 ACM.
9781450312202
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11697/112033
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 22
  • ???jsp.display-item.citation.isi??? 17
social impact