Dynamic-Stochastic Influence Diagrams: Integrating Time-Slices IDs and Discrete Event Systems Modeling
The Influence Diagrams (IDs) is a kind of Probabilistic Belief Networks for graphic modeling. The usage of IDs can improve the communication among field experts, modelers, and decision makers, by showing the issue frame discussed from a high-level point of view. This paper enhances the Time-Sliced Influence Diagrams (TSIDs, or called Dynamic IDs) based formalism from a Discrete Event Systems Modeling and Simulation (DES M&S) perspective, for Exploring Analysis (EA) modeling. The enhancements enable a modeler to specify times occurred of endogenous events dynamically with stochastic sampling as model running and to describe the inter- influences among them with variable nodes in a dynamic situation that the existing TSIDs fails to capture. The new class of model is named Dynamic-Stochastic Influence Diagrams (DSIDs). The paper includes a description of the modeling formalism and the hiberarchy simulators implementing its simulation algorithm, and shows a case study to illustrate its enhancements.
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 Paul K. Davis, "Exploratory Analysis Enabled by Multiresolution, Multiperspective Modeling", In Proceedings of the 2000 Winter Simulation Conference.
 Paul K. Davis, "New Paradigms and New Challenges", In Proceedings of the 2005 Winter Simulation Conference.
 Paul K. Davis, "Lessons from Defense Planning and Analysis for Thinking About Systems of Systems", Prepared for the Symposium on Complex System Engineering, Santa Monica, Calif.: RAND Corporation, 2007.
 Paul K. Davis, "Amy Henninger. Analysis, Analysis Practices, and Implications for Modeling and Simulation", Santa Monica, Calif.: RAND Corporation, 2007. pp. 5-6.
 Analytica User Guide, Lumina Decision Systems, Inc.
 M. Granger Morgan and Max Henrion, "Chapter 10 of Uncertainty: A Guide to Dealing with Uncertainty in Quantitative Risk and Policy Analysis", Cambridge University Press, New York, 1990, reprinted in 1998.
 R.A. Howard, J.E. Matheson, "The Principles and Applications of Decision Analysis", vol. II, Strategic Decisions Group, Menlo Park, CA, 1984, pp. 720-762 (Chapter: influence diagrams).
 Figueroa, G. A., and Sucar, L. E., "A Temporal Bayesian Network for Diagnosis and Prediction", In Proceedings of the 15th Conference on Uncertainty in Artificial Intelligence, 1999.
 Murphy, K., "Dynamic Bayesian Networks: Representation", Inference and Learning, PhD Thesis, UC Berkley, Jul. 2002.
 P.K, D., J.H. Bigelow, and J. McEver, "EXHALT: An Interdiction Model for Exploring Halt Capabilities in a Large Scenario Space", Vol. MR-1137- OSD. 2000, Santa Monica, CA: RAND.
 Davis, P.K., J.H. Bigelow, and J. McEver, "Exploratory Analysis and a Case History of Multiresolution, Multiperspective Modeling", reprint volume RP-925. 2001, Santa Monica: RAND.
 Miguel L├│pez-D├¡az, Luis J, "Rodr├¡guez-Mu├▒iz. Influence Diagrams with Super Value Nodes Involving Imprecise Information", European Journal of Operational Research (S0377-2217), 2007, 179: 203-219.
 Michael Diehl, Yacov Y, "Haimes. Influence Diagrams with Multiple Objectives and Tradeoff Analysis ", IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans (S1083-4427), Vol. 34, NO. 3, May 2004:293-304.
 R.D. Shachter, M.A. Peot, "Decision Making Using Probabilistic Inference Methods", in: Proceedings of the 8th Conference on Uncertainty in Artificial Intelligence, San Jose, 1992, pp. 276-283.
 R.D. Shachter, P.P Ndilikilikesha, "Using Potential Influence Diagrams for Probabilistic Inference and Decision Making", in: Proceedings of the 9th Conference on Uncertainty and Artificial Intelligence, 1993, pp. 383-390.
 P.P. Ndilikilikesha, "Potential Influence Diagrams", International Journal of Approximate Reasoning 11 (1994) 251-285.
 Koller D, Milch B, "Multi-agent Influence Diagrams for Representing and Solving Games". IJCAI. Seattle,USA: Elsevier. 2001: 1024-1034.
 N.L. Zhang, "Probabilistic Inference in Influence Diagrams", in: Proceedings of the 14th Annual Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, 1998, pp. 514-522.
 Paul A.Fishwick, "Simulation Model Design and Execution", Prentice- Hall Inc, 1995.
 Eric L. Savage, Lee W. Schruben, and Enver Y├╝cesan, "On the Generality of Event-Graph Models", INFORMS Journal on Computing, Vol. 17, No. 1, Winter 2005, pp. 3- 9.
 Zeigler, B. P., T. G. Kim and H. Praehofer, "Theory of Modeling and Simulation: Integrating Discrete Event and Continuous Complex Dynamic Systems", second edition Academic Press, 2000.
 ZHAO Xin, ZHANG Wei, LEI Yong-lin, ZHU Yi-fan, "Time-Sliced Influence Diagrams for Analytical Modeling", The 2nd IEEE International Conference on Advanced Computer Control, 27-29, March 2010, Shenyang, China.