A Comparison of Graphical Techniques for Asymmetric Decision Problems

References

• Bielza C., Shenoy P. P. A comparison of graphical techniques for asymmetric decision problems: Supplement to management science paper. (1998) . Working paper no. 282, School of Business, University of Kansas, Lawrence, KS. Available by ftp from ftp.bschool.ukans.edu/home/pshenoy/wp282.psGoogle Scholar
• Buede D. Aiding insight III. OR/MS Today (1996) 23(4):73–79Google Scholar
• Call H. J., Miller W. A. A comparison of approaches and implementations for automating decision analysis. Reliability Engrg. System Safety (1990) 30:115–162Crossref, Google Scholar
• Covaliu Z. Sequential diagrams and influence diagrams: A complementary relationship for modeling and solving decision problems. Bayesian Statistics 5 (1996) (Oxford University Press)521–532Google Scholar
• Covaliu Z., Oliver R. M. Representation and solution of decision problems using sequential decision diagrams. Management Sci. (1995) 41(12):1860–1881Link, Google Scholar
• Cowell R. G. Decision networks: A new formulation for multistage decision problems. (1994) . Research report no. 132, University College, London, UKGoogle Scholar
• Fung R. M., Shachter R. D. article-title>Contingent influence diagrams. (1990) . Working paper Department of Engineering-Economic Systems, Stanford University, Stanford, CAGoogle Scholar
• Goutis C. A graphical method for solving a decision analysis problem. IEEE Trans. Systems, Man, Cybernetics (1995) 25(8):1181–1193Crossref, Google Scholar
• Guo R., Shenoy P. P. A note on Kirkwood's algebraic method for decision problems. Eur. J. Oper. Res. (1996) 93:628–638Crossref, Google Scholar
• Jensen F., Jensen F. V., Dittmer S. L., Mantaras R. L., Poole D. From influence diagrams to junction trees. Uncertainty in Artificial Intelligence: Proc. Tenth Conf. (1994) (Morgan Kaufmann, San Francisco, CA) 367–373Crossref, Google Scholar
• Kirkwood C. W. An algebraic approach to formulating and solving large models for sequential decisions under uncertainty. Management Sci. (1993) 39(7):900–913Link, Google Scholar
• Olmsted S. M. On representing and solving decision problems. (1983) . Ph.D. thesis, Department of Engineering-Economic Systems, Stanford University, Stanford, CAGoogle Scholar
• Howard R. A., Matheson J. E., Howard R. A., Matheson J. E. Influence diagrams. The Principles and Applications of Decision Analysis (1981) 2(1984, Strategic Decisions Group, Menlo Park, CA) 719–762Google Scholar
• Ndilikilikesha P. C. Potential influence diagrams. Internat. J. Approximate Reasoning (1994) 10(3):251–285Crossref, Google Scholar
• Qi R., Zhang L., Poole D., Mantaras R. L., Poole D. Solving asymmetric decision problems with influence diagrams. Uncertainty in Artificial Intelligence: Proc. Tenth Conf. (1994) (Morgan Kaufmann, San Francisco, CA) 491–497Crossref, Google Scholar
• Shachter R. D. Evaluating influence diagrams. Oper. Res. (1986) 34(6):871–882Link, Google Scholar
• Shachter R. D. Probabilistic inference and influence diagrams. Oper. Res. (1988) 36(4):589–604Link, Google Scholar
• Shachter R. D., Peot M. A., Dubois D., Wellman M. P., D'Ambrosio B., Smets P. Decision making using probabilistic inference methods. Uncertainty in Artificial Intelligence: Proc. Eighth Conf. (1992) (Morgan Kaufmann, San Mateo, CA) 276–283Crossref, Google Scholar
• Shenoy P. P. Valuation-based systems for Bayesian decision analysis. Oper. Res. (1992) 40(3):463–484Link, Google Scholar
• Shenoy P. P., Hand D. J. A new method for representing and solving Bayesian decision problems. Artificial Intelligence Frontiers in Statistics: AI and Statistics III (1993a) (Chapman & Hall, London, UK) 119–138Crossref, Google Scholar
• Shenoy P. P. Valuation network representation and solution of asymmetric decision problems. Eur. J. Oper. Res. (1993b) 121(3):146–174Available by ftp from ⟨ ftp.bschool.ukans.edu/home/pshenoy/wp246.ps⟩Google Scholar
• Shenoy P. P. A comparison of graphical techniques for decision analysis. Eur. J. Oper. Res. (1994a) 78(1):1–21Crossref, Google Scholar
• Shenoy P. P. Consistency in valuation-based systems. ORSA J. Comput. (1994b) 6(3):281–291Link, Google Scholar
• Shenoy P. P. Representing conditional independence relations by valuation networks. Internat. J. Uncertainty, Fuzziness and Knowledge-Based Systems (1994c) 2(2):143–165Crossref, Google Scholar
• Shenoy P. P., Besnard P., Hanks S. A new pruning method for solving decision trees and game trees. Uncertainty in Artificial Intelligence: Proc. Eleventh Conf. (1995) (Morgan Kaufmann, San Francisco, CA) 482–490Google Scholar
• Shenoy P. P., Fisher D., Lenz H.-J. Representing and solving asymmetric decision problems using valuation networks. Learning from Data: Artificial Intelligence and Statistics V. Lecture Notes in Statistics (1996) 112(Springer-Verlag, New York) 99–108Crossref, Google Scholar
• Shenoy P. P. Game trees for decision analysis. Theory and Decision (1998) 44:149–177Crossref, Google Scholar
• Shenoy P. P., Shafer G., Shachter R. D., Levitt T. S., Lemmer J. F., Kanal L. N. Axioms for probability and belief-function propagation. Uncertainty in Artificial Intelligence (1990) 4(North-Holland, Amsterdam) 169–198Crossref, Google Scholar
• Smith J. Q. Influence diagrams for Bayesian decision analysis. Eur. J. Oper. Res. (1989) 40:363–376Crossref, Google Scholar
• Smith J. E., Holtzman S., Matheson J. E. Structuring conditional relationships in influence diagrams. Oper. Res. (1993) 41(2):280–297Link, Google Scholar
• Tatman J. A., Shachter R. D. Dynamic programming and influence diagrams. IEEE Trans. Systems, Man, Cybernetics (1990) 20(2):365–379Crossref, Google Scholar
• von Neumann J., Morgenstern O.Theory of Games and Economic Behavior (1944) 1st ed.(Princeton University Press, Princeton, NJ) Google Scholar
• Zhang N. L., Qi R., Poole D. A computational theory of decision networks. Internat. J. Approximate Reasoning (1994) 11(2):83–158Crossref, Google Scholar