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• W1552701195 abstract "A new graphical framework, Abridged Petri Nets (APNs) is introduced for bottom-up modeling of complex stochastic systems. APNs are similar to Stochastic Petri Nets (SPNs) in as much as they both rely on component-based representation of system state space, in contrast to Markov chains that explicitly model the states of an entire system. In both frameworks, so-called tokens (denoted as small circles) represent individual entities comprising the system; however, SPN graphs contain two distinct types of nodes (called places and transitions) with transitions serving the purpose of routing tokens among places. As a result, a pair of place nodes in SPNs can be linked to each other only via a transient stop, a transition node. In contrast, APN graphs link place nodes directly by arcs (transitions), similar to state space diagrams for Markov chains, and separate transition nodes are not needed. Tokens in APN are distinct and have labels that can assume both discrete values (“colors”) and continuous values (“ages”), both of which can change during simulation. Component interactions are modeled in APNs using triggers, which are either inhibitors or enablers (the inhibitors’ opposites). Hierarchical construction of APNs rely on using stacks (layers) of submodels with automatically matching color policies. As a result, APNs provide at least the same modeling power as SPNs, but, as demonstrated by means of several examples, the resulting models are often more compact and transparent, therefore facilitating more efficient performance evaluation of complex systems. 1 Background The paper introduces Abridged Petri Nets (APNs), a new graphical framework for modeling the stochastic behavior of complex systems that consist of multiple interacting components. This framework can be considered as a derivative of Stochastic Petri Nets (SPNs) [2] that aims at retaining SPNs versatility in terms of modeling power, while streamlining the choice of the modeling building blocks. The visual clutter and often confusing choices that are often perceived as the major obstacle to the larger success of SPNs are reduced [1], resulting in simpler and more transparent models that can be built using only graphical interface." @default.
• W1552701195 created "2016-06-24" @default.
• W1552701195 creator A5015960229 @default.
• W1552701195 date "2013-12-10" @default.
• W1552701195 modified "2023-09-27" @default.
• W1552701195 title "Abridged Petri Nets" @default.
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