Creating executable models of influence nets with colored Petri nets

被引：22

作者：

Wagenhals L.W. ^{[1
]}

Shin I. ^{[1
]}

Levis A.H. ^{[1
]}

机构：

[1] System Architectures Laboratory, George Mason University, Fairfax, VA 22030

来源：

International Journal on Software Tools for Technology Transfer | 1998年 / 2卷 / 2期

关键词：

Bayesian nets; Colored Petri nets; Course of action; Discrete event dynamical systems; High-level Petri nets; Influence nets;

D O I：

10.1007/s100090050025

中图分类号：

学科分类号：

摘要：

This paper describes a methodology for converting an influence net to a dynamic executable model, implemented using the colored Petri net (CPN) formalism and tools, so that it can be used to assess the impact of a set of controllable events or actions on outcomes of interest; specifically, assess the impact of various sequences and timing of those actionable events. With this methodology, alternative courses of action, first defined using influence nets, which are static equilibrium models, can be refined by adding sequence and timing information for analysis and comparison. The techniques developed offer a means to integrate intelligence and operational planning models to improve course of action development. The paper includes a description of the automated algorithms that convert an influence net to a colored Petri net and illustrates how that model can be used for the analysis of alternative courses of action. © 1998 Springer-Verlag.

引用

页码：168 / 181

页数：13

共 6 条

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[2] Chang K.C., Lehner P.E., Levis A.H., Zaidi S.A.K., Zhao X., On Causal Influence Logic, (1994)
[3] Jensen J.V., An Introduction of Bayesian Networks, pp. 48-52, (1996)
[4] Kristensen L.M., Christensen S., Jensen K., The practitioner's guide to coloured Petri nets, Software Tools for Technology Transfer, 2, 2, pp. 98-132, (1998)
[5] Rosen J.A., Smith W.L., Influence net modeling with causal strengths: An evolutionary approach, Proc. Command and Control Research and Technology Symposium, pp. 699-708, (1996)
[6] Wilkstrom A., Functional Programming Using Standard ML, (1987)

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