Publication

[SackZ12] A General Framework for Probabilistic Characterizing Formulae Sack, J. and Zhang, L. In Verification, Model Checking, and Abstract Interpretation - 13th International Conference (VMCAI), pages 396-411, Springer, Lecture Notes in Computer Science 7148, 2012.
Downloads: pdf, bibURL: http://dx.doi.org/10.1007/978-3-642-27940-9_26 Abstract. 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.