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We describe the main features of S m A r T, a software package providing a seamless environment for the logic and probabilistic analysis of complex systems. S m A r T can combine different formalisms in the same modeling study. For the analysis of logical behavior, both explicit and symbolic state-space generation techniques, as well as symbolic CTL(More)
We discuss how to describe the Markov c hain underlying a generalized stochastic Petri net using Kro-necker operators on smaller matrices. We extend previous approaches by allowing both an extensive type of marking-dependent behavior for the transitions and the presence of immediate synchronizations. The derivation of the results is thoroughly formalized,(More)
We present a novel algorithm for generating state spaces of asynchronous systems using Multi–valued Decision Diagrams. In contrast to related work, we encode the next–state function of a system not as a single Boolean function, but as cross–products of integer functions. This permits the application of various iteration strategies to build a system's state(More)
Recent developments in the use of Kronecker algebra for the solution of continuous-time Markov chains (CTMCs), in particular models based on stochastic Petri nets, have increased the size of the systems that can be analyzed using exact numerical methods. We report on the more recent results, and employ them to study a kanban system.
We present a decomposition approach for the solution of large stochastic reward nets (SRNs) based on the concept of near-independence. The overall model consists of a set of submodels whose interactions are described by an import graph. Each node of the graph corresponds to a parametric SRN submodel and an arc from submodel A to submodel B corresponds to a(More)
— Stochastic Petri nets (SPNs) with generally distributed firing times can model a large class of systems, but simulation is the only feasible approach for their solution. We explore a hierarchy of SPN classes where modeling power is reduced in exchange for an increasingly efficient solution. Generalized stochastic Petri nets (GSPNs), deterministic and(More)
We present a new technique for the generation and storage of the reachability set of a Petri net. Our approach is inspired by previous work on Binary and Multi-valued Decision Diagrams but exploits a concept of locality for the effect of a transition's firing to vastly improve algorithmic performance. The result is a data structure and a set of manipulation(More)