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.
[1] Paul K. Davis, "Exploratory Analysis Enabled by Multiresolution, Multiperspective
Modeling", In Proceedings of the 2000 Winter Simulation
Conference.
[2] Paul K. Davis, "New Paradigms and New Challenges", In Proceedings of
the 2005 Winter Simulation Conference.
[3] 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.
[4] Paul K. Davis, "Amy Henninger. Analysis, Analysis Practices, and Implications
for Modeling and Simulation", Santa Monica, Calif.: RAND
Corporation, 2007. pp. 5-6.
[5] Analytica User Guide, Lumina Decision Systems, Inc.
[6] 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.
[7] 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).
[8] 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.
[9] Murphy, K., "Dynamic Bayesian Networks: Representation", Inference
and Learning, PhD Thesis, UC Berkley, Jul. 2002.
[10] 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.
[11] 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.
[12] Miguel López-Díaz, Luis J, "Rodríguez-Muñiz. Influence Diagrams with
Super Value Nodes Involving Imprecise Information", European Journal
of Operational Research (S0377-2217), 2007, 179: 203-219.
[13] 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.
[14] 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.
[15] 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.
[16] P.P. Ndilikilikesha, "Potential Influence Diagrams", International Journal
of Approximate Reasoning 11 (1994) 251-285.
[17] Koller D, Milch B, "Multi-agent Influence Diagrams for Representing
and Solving Games". IJCAI. Seattle,USA: Elsevier. 2001: 1024-1034.
[18] 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.
[19] Paul A.Fishwick, "Simulation Model Design and Execution", Prentice-
Hall Inc, 1995.
[20] 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.
[21] 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.
[22] 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.
[1] Paul K. Davis, "Exploratory Analysis Enabled by Multiresolution, Multiperspective
Modeling", In Proceedings of the 2000 Winter Simulation
Conference.
[2] Paul K. Davis, "New Paradigms and New Challenges", In Proceedings of
the 2005 Winter Simulation Conference.
[3] 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.
[4] Paul K. Davis, "Amy Henninger. Analysis, Analysis Practices, and Implications
for Modeling and Simulation", Santa Monica, Calif.: RAND
Corporation, 2007. pp. 5-6.
[5] Analytica User Guide, Lumina Decision Systems, Inc.
[6] 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.
[7] 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).
[8] 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.
[9] Murphy, K., "Dynamic Bayesian Networks: Representation", Inference
and Learning, PhD Thesis, UC Berkley, Jul. 2002.
[10] 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.
[11] 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.
[12] Miguel López-Díaz, Luis J, "Rodríguez-Muñiz. Influence Diagrams with
Super Value Nodes Involving Imprecise Information", European Journal
of Operational Research (S0377-2217), 2007, 179: 203-219.
[13] 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.
[14] 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.
[15] 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.
[16] P.P. Ndilikilikesha, "Potential Influence Diagrams", International Journal
of Approximate Reasoning 11 (1994) 251-285.
[17] Koller D, Milch B, "Multi-agent Influence Diagrams for Representing
and Solving Games". IJCAI. Seattle,USA: Elsevier. 2001: 1024-1034.
[18] 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.
[19] Paul A.Fishwick, "Simulation Model Design and Execution", Prentice-
Hall Inc, 1995.
[20] 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.
[21] 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.
[22] 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.
@article{"International Journal of Engineering, Mathematical and Physical Sciences:55766", author = "Xin Zhao and Yin-fan Zhu and Wei-ping Wang and Qun Li", title = "Dynamic-Stochastic Influence Diagrams: Integrating Time-Slices IDs and Discrete Event Systems Modeling", abstract = "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.
", keywords = "Time-sliced influence diagrams, discrete event systems, dynamic-stochastic influence diagrams, modeling formalism, simulation algorithm.", volume = "4", number = "7", pages = "891-8", }
