Decision diagrams are a family of data structures that can compactly encode many functions on discrete structured domains, that is, domains that are the cross-product of finite sets. We present some important classes of decision diagrams and show how they can be effectively employed to derive "symbolic" algorithms for the analysis of large discrete-state models. In particular, we discuss both explicit and symbolic algorithms for state-space generation, CTL model-checking, and continuous-time Markov chain solution. We conclude with some suggestions for future research directions. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Ciardo, G. (2007). Data representation and efficient solution: A decision diagram approach. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4486 LNCS, pp. 371–394). Springer Verlag. https://doi.org/10.1007/978-3-540-72522-0_9
Mendeley helps you to discover research relevant for your work.