Recently, a general framework on characteristic formulae was proposed by Aceto et al. It offers a simple theory that allows one to easily obtain characteristic formulae of many non-probabilistic behavioral relations. Our paper studies their techniques in a probabilistic setting. We provide a general method for determining characteristic formulae of behavioral relations for probabilistic automata using fixed-point probability logics. We consider such behavioral relations as simulations and bisimulations, probabilistic bisimulations, probabilistic weak simulations, and probabilistic forward simulations. This paper shows how their constructions and proofs can follow from a single common technique. © 2012 Springer-Verlag.
CITATION STYLE
Sack, J., & Zhang, L. (2012). A general framework for probabilistic characterizing formulae. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7148 LNCS, pp. 396–411). https://doi.org/10.1007/978-3-642-27940-9_26
Mendeley helps you to discover research relevant for your work.