Contents I. Introduction
Logical networks (LNs) and logical control networks (LCNs) are famous models for their effectiveness to simulate gene regulatory networks [1], networked evolutionary games [2], smart home [3], IC engines [4], and so on. One discovers that bacteriophage performs two configurations, that is, lysis and lysogeny, according to intrinsic environments and extrinsic perturbations, and its state evolution can be determined by time-varying logical functions [5]. In consideration of the fact that uncertain phenomenon is inevitable in almost all practical situations, Markovian jump LNs (MJLNs) and Markovian jump LCNs (MJLCNs) were recently proposed in [6], which not only maintain the original advantages of deterministic models but also cope with the rule-based uncertainties. In form, MJLNs [respectively (resp.), MJLCNs] can be regarded as the extension of determined LNs (resp., LCNs) and probabilistic LNs (resp., probabilistic LCNs).
