Efficient state space generation of GSPNs using decision diagrams

A. Miner
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引用次数: 12

Abstract

Implicit techniques for representing and generating the reachability set of a high-level model have become quite efficient. However, such techniques are usually restricted to models whose events have equal priority. Models containing events with differing classes of priority or complex priority structure, in particular models with immediate events, have thus been required to use explicit reachability set generation techniques. In this paper, we present an efficient implicit technique, based on multi-valued decision diagram representations for sets of states and matrix diagram representations for next-state functions, that can handle models with complex priority structure. If the model contains immediate events, the vanishing states can be eliminated either during generation, by manipulating the matrix diagram, or after generation, by manipulating the multi-valued decision diagram. We apply both techniques to several models and give detailed results.
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利用决策图高效地生成gspn的状态空间
表示和生成高级模型的可达性集的隐式技术已经变得相当有效。然而,这些技术通常仅限于事件具有同等优先级的模型。因此,包含具有不同优先级或复杂优先级结构的事件的模型,特别是具有即时事件的模型,需要使用显式可达性集生成技术。本文提出了一种基于状态集的多值决策图表示和下一状态函数的矩阵图表示的有效隐式技术,可以处理具有复杂优先级结构的模型。如果模型包含即时事件,则可以在生成过程中通过操作矩阵图或在生成之后通过操作多值决策图来消除消失状态。我们将这两种技术应用于几个模型,并给出了详细的结果。
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