Modeling challenges with influence diagrams: Constructing probability and utility models

Decision Support Systems

Volumn 49, Issue 4, 2010, Pages 354-364

  Bielza, C ^a   Gomez M ^b   Shenoy, P P ^c  

^a UNIVERSIDAD POLITÉCNICA DE MADRID   (Spain)

^b UNIVERSITY OF GRANADA   (Spain)

^c UNIVERSITY OF KANSAS   (United States)

Author keywords

Bayesian networks; Ceteris paribus networks; Decision making under uncertainty; Expected utility networks; Generalized additive independence networks; Influence diagrams; Probabilistic graphical models; Utility ceteris paribus networks; Utility diagrams

Indexed keywords

CETERIS PARIBUS NETWORKS; DECISION-MAKING UNDER UNCERTAINTY; EXPECTED UTILITY; GENERALIZED ADDITIVE INDEPENDENCE NETWORKS; INFLUENCE DIAGRAM; PROBABILISTIC GRAPHICAL MODELS; UTILITY CETERIS PARIBUS NETWORKS; UTILITY DIAGRAMS;

DECISION MAKING; DISTRIBUTED PARAMETER NETWORKS; GRAPHIC METHODS; INFERENCE ENGINES; INTELLIGENT NETWORKS; PROBABILITY; SPEECH RECOGNITION;

BAYESIAN NETWORKS;

EID: 77955847649     PISSN: 01679236     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.dss.2010.04.003     Document Type: Article

References (95)

1. A. Abbas, and R. Howard Attribute dominance utility Decision Analysis 2 2005 185 206
2. F. Bacchus, and A. Grove Graphical models for preference and utility P. Besnard, S. Hanks, Uncertainty in Artificial Intelligence: Proceedings of the 11th Conference 1995 Morgan Kaufmann San Francisco, CA 3 10
3. F. Bacchus, and A. Grove Utility independence in qualitative decision theory L. Aiello, J. Doyle, S. Shapiro, Proceedings of the 5th International Conference on Principles of Knowledge Representation and Reasoning 1996 Morgan Kaufmann San Francisco, CA 542 552
4. C. Bielza, M. Gómez, S. Ríos-Insua, and J. Fernández del Pozo Structural, elicitation and computational issues faced when solving complex decision problems with influence diagrams Computers and Operations Research 27 2000 725 740
5. C. Bielza, M. Gómez, and P. Shenoy Modeling Challenges with Influence Diagrams: Representation Issues, Working Paper 319 2009 School of Business University of Kansas, Lawrence, KS
6. C. Bielza, D. Ríos Insua, and S. Ríos-Insua Influence diagrams under partial information J. Bernardo, J. Berger, A. Dawid, A. Smith, Bayesian Statistics 5 1996 Oxford U.P 491 497
7. C. Bielza, S. Ríos-Insua, M. Gómez, and J.F. del Pozo Sensitivity analysis in IctNeo D. Ríos Insua, F. Ruggeri, Robust Bayesian Analysis Lecture Notes in Statistics 152 2000 Springer 317 334
8. L. Bobrowski HEPAR: Computer System for Diagnosis Support and Data Analysis, Technical Report, Institute of Biocybernetics and Biomedical Engineering 1992 Polish Academy of Sciences Warsaw, Poland
9. M. Bohanec, and B. Zupan A function-decomposition method for development of hierarchical multi-attribute decision models Decision Support Systems 36 2004 215 233
10. C. Boutilier The influence of influence diagrams on artificial intelligence Decision Analysis 2 2005 229 231
11. C. Boutilier, F. Bacchus, and R. Brafman UCP-networks: A directed graphical representation of conditional utilities J. Breese, D. Koller, Uncertainty in Artificial Intelligence: Proceedings of the 17th Conference 2001 Morgan Kaufmann San Francisco, CA 56 64
12. C. Boutilier, R. Brafman, C. Domshlak, H. Hoos, and D. Poole CP-nets: A tool for representing and reasoning with conditional ceteris paribus preference statements Journal of AI Research 21 2004 135 191
13. R. Brafman, and Y. Dimopoulos Extended semantics and optimization algorithms for CP-networks Computational Intelligence 20 2004 218 245
14. R. Brafman, C. Domshlak, and S. Shimony On graphical modeling of preference and importance Journal of Artificial Intelligence Research 25 2006 389 424
15. D. Buede Influence diagrams: A practitioner's perspective Decision Analysis 2 2005 235 237
16. A. Cano, M. Gómez, and S. Moral A forward-backward Monte Carlo method for solving influence diagrams International Journal of Approximate Reasoning 42 2006 119 135
17. U. Chajewska, L. Getoor, J. Norman, and Y. Shahar Utility elicitation as a classification problem G. Cooper, S. Moral, Uncertainty in Artificial Intelligence: Proceedings of the 14th Conference 1998 Morgan Kaufmann San Francisco, CA 79 88
18. U. Chajewska, and D. Koller Utilities as random variables: Density estimation and structure discovery C. Boutilier, M. Goldszmidt, Uncertainty in Artificial Intelligence: Proceedings of the 16th Conference 2000 Morgan Kaufmann San Francisco, CA 63 71
19. U. Chajewska, D. Koller, and D. Ormoneit Learning an agent's utility function by observing behavior C. Brodley, A. Danyluk, Proceedings of the 18th International Conference on Machine Learning 2001 Morgan Kaufmann San Francisco, CA 35 42
20. U. Chajewska, D. Koller, and R. Parr Making rational decisions using adaptive utility elicitation H. Kautz, B. Porter, Proceedings of the 17th National Conference on Artificial Intelligence 2000 AAAI Press Menlo Park, CA 363 369
21. J. Charnes, and P. Shenoy Multi-stage Monte Carlo method for solving influence diagrams using local computation Management Science 50 2004 405 418
22. G. Cooper A method for using belief networks as influence diagrams, in: Proceedings of the 4th Conference on Uncertainty in Artificial Intelligence 1988 University of Minnesota Minneapolis pp. 55-63
23. M. Diehl, and Y. Haimes Influence diagrams with multiple objectives and tradeoff analysis IEEE Transactions on Systems, Man, and Cybernetics 34 2004 293 304
24. F. Díez Parameter adjustment in bayes networks. the generalized noisy OR-gate D. Heckerman, A. Mamdani, Uncertainty in Artificial Intelligence: Proceedings of the Ninth Conference 1993 Morgan Kaufmann San Francisco, CA 99 105
25. M. Druzdzel, and R. Flynn Decision support systems A. Kent, Encyclopedia of Library and Information Science volume 67 2000 Marcel Dekker 120 133
26. M. Druzdzel, and L. van der Gaag Building probabilistic networks: 'where do the numbers come from?' guest editors' introduction IEEE Transactions on Knowledge and Data Engineering 12 2000 481 486
27. P. Farquhar Utility assessment methods Management Science 30 1984 1283 1300
28. J. Felli, and G. Hazen Sensitivity analysis and the expected value of perfect information Medical Decision Making 18 1998 95 109
29. J. Fernández del Pozo, and C. Bielza An interactive framework for open queries in decision support systems F. Garijo, J. Riquelme, M. Toro, Advances in Artificial Intelligence-IBERAMIA 2002, Lecture Notes in Artificial Intelligence 2527 2002 Springer Berlin 254 264
30. J. Fernández del Pozo, C. Bielza, and M. Gómez A list-based compact representation for large decision tables management European Journal of Operational Research 160 2005 638 662
31. K.W. Fertig, and J.S. Breese Probability intervals over influence diagrams IEEE Transactions on Pattern Analysis and Machine Intelligence 15 1993 280 286
32. L. van der Gaag, S. Renooij, C. Witteman, B. Aleman, and B. Taal How to elicit many probabilities K. Laskey, H. Prade, Uncertainty in Artificial Intelligence: Proceedings of the 15th Conference 1999 Morgan Kaufmann San Fransisco, CA 647 654
33. L. van der Gaag, S. Renooij, C. Witteman, B. Aleman, and B. Taal Probabilities for a probabilistic network: A case study of oesophageal cancer Artificial Intelligence in Medicine 25 2002 123 148
34. L. Garcia, and R. Sabbadin Complexity results and algorithms for possibilistic influence diagrams Artificial Intelligence 172 2008 1018 1044
35. G. Geiger An axiomatic account of status quo-dependent non-expected utility: Pragmatic constraints on rational choice under risk Mathematical Social Sciences 55 2008 116 142
36. M. Gómez, and C. Bielza Node deletion sequences in influence diagrams using genetic algorithms Statistics & Computing 14 2004 181 198
37. M. Gómez, C. Bielza, J. Fernández del Pozo, and S. Ríos-Insua A graphical decision-theoretic model for neonatal jaundice Medical Decision Making 27 2007 250 265
38. C. Gonzales, and P. Perny GAI networks for utility elicitation D. Dubois, C. Welty, M.A. Williams, Proceedings of the 9th International Conference on the Principles of Knowledge Representation and Reasoning 2004 AAAI Press Menlo Park, CA 224 234
39. W. Guezguez, N. Ben Amor, and K. Mellouli Qualitative possibilistic influence diagrams based on qualitative possibilistic utilities European Journal of Operational Research 195 2008 223 238
40. P. Haddawy, V. Ha, A. Restificar, B. Geisler, and J. Miyamoto Preference elicitation via theory refinement Journal of Machine Learning Research 4 2003 317 337
41. D. Heckerman, and J. Breese Causal independence for probability assessment and inference using Bayesian networks IEEE Transactions on Systems, Man, and Cybernetics 26 1996 826 831
42. M. Henrion Some practical issues in constructing belief networks L. Kanal, T. Levitt, J. Lemmer, Uncertainty in Artificial Intelligence 3 1989 North-Holland Amsterdam 161 172
43. J. Hershey, H. Kunreuther, and P. Schoemaker Sources of bias in assessment procedures for utility functions Management Science 28 1982 936 953
44. C. Staël von Holstein, and J. Matheson A Manual for Encoding Probability Distributions 1979 SRI International Palo Alto, Calif.
45. M. Horsch, and D. Poole Flexible policy construction by information refinement E. Horvitz, F. Jensen, Uncertainty in Artificial Intelligence: Proceedings of the 12th Conference 1996 Morgan Kaufmann San Francisco, CA 315 324
46. M. Horsch, and D. Poole An anytime algorithm for decision making under uncertainty G. Cooper, S. Moral, Uncertainty in Artificial Intelligence: Proceedings of the 14th Conference 1998 Morgan Kaufmann San Francisco 246 255
47. M. Horsch, and D. Poole Estimating the value of computation in flexible information refinement K. Laskey, H. Prade, Uncertainty in Artificial Intelligence: Proceedings of the 15th Conference 1999 Morgan Kaufmann San Francisco, CA 297 304
48. R. Howard, and J. Matheson Influence diagrams R. Howard, J. Matheson, Readings on the Principles and Applications of Decision Analysis, volume II, Strategic Decisions Group 1984 719 762
49. J. Jaffray Some experimental findings on decision making under risk and their implications European Journal of Operational Research 38 1989 301 306
50. F. Jensen, F. Jensen, and D. Dittmer From influence diagrams to junction trees R. de Mantaras, D. Poole, Uncertainty in Artificial Intelligence: Proceedings of the 10th Conference 1994 Morgan Kaufmann San Francisco, CA 367 373
51. F. Jensen, and T. Nielsen Bayesian Networks and Decision Graphs second edition 2007 Springer New York, NY
52. E. Johnson, and D. Schkade Bias in utility assessments: Further evidence and explanations Management Science 35 1989 406 424
53. D. Kahneman, P. Slovic, and A. Tversky Judgement under Uncertainty: Heuristic and Biases 1982 Cambridge University Press
54. D. Kahneman, and A. Tversky Prospect theory: An analysis of decision under risk Econometrica 47 1979 763 791
55. H.Y. Kao Diagnostic reasoning and medical decision-making with fuzzy influence diagrams Computer Methods and Programs in Biomedicine 90 2008 9 16
56. R. Keeney, and R. Gregory Selecting attributes to measure the achievement of objectives Operations Research 53 2005 1 11
57. R. Keeney, and H. Raiffa Decisions with Multiple Objectives: Preferences and Value Tradeoffs second edition 1993 Cambridge University Press Cambridge, UK
58. K. Korb, and A. Nicholson Bayesian Artificial Intelligence 2004 Chapman and Hall/CRC London, UK
59. M. Kuppermann, S. Shiboski, D. Feeny, E. Elkin, and A. Washington Can preference scores for discrete states be used to derive preference scores for an entire path of events? Medical Decision Making 17 1997 42 55
60. P. La Mura, and Y. Shoham Expected utility networks K. Laskey, H. Prade, Uncertainty in Artificial Intelligence: Proceedings of the 15th Conference 1999 Morgan Kaufmann San Francisco, CA 366 373
61. S. Lauritzen, and D. Nilsson Representing and solving decision problems with limited information Management Science 47 2001 1235 1251
62. A. Madsen, and F. Jensen Lazy evaluation of symmetric Bayesian decision problems K. Laskey, H. Prade, Uncertainty in Artificial Intelligence: Proceedings of the 15th Conference 1999 Morgan Kaufmann San Francisco, CA 382 390
63. M. McCord, and R. de Neufville "Lottery equivalents": Reduction of the certainty effect problem in utility assessment Management Science 32 1986 56 60
64. M. McGeachie, and J. Doyle Efficient utility functions for ceteris paribus preferences 18th National Conference on Artificial Intelligence 2002 Edmonton Canada 279 284
65. M. Morgan, and M. Henrion Uncertainty: A Guide to Dealing with Uncertainty in Quantitative Risk and Policy Analysis 1990 Cambridge University Press
66. R. Nease, and D. Owens Use of influence diagrams to structure medical decisions Medical Decision Making 17 1997 263 275
67. R. Nelsen An Introduction to Copulas 1998 Springer New York, NY
68. T. Nielsen, and F. Jensen Sensitivity analysis in influence diagrams IEEE Transactions on Systems, Man and Cybernetics 33 2003 223 234
69. T. Nielsen, and F. Jensen Learning a decision maker's utility function from (possibly) inconsistent behavior Artificial Intelligence 160 2004 53 78
70. S. Olmsted, On representing and solving decision problems, Ph.D. thesis, Department of Engineering-Economic Systems, Stanford, CA., 1983.
71. A. Oniśko, M. Druzdzel, H. Wasyluk, A probabilistic causal model for diagnosis of liver disorders, in: Proceedings of the Seventh International Symposium on Intelligent Information Systems (IIS-98), pp. 379-387.
72. A. Oniśko, M. Druzdzel, and H. Wasyluk Learning Bayesian network parameters from small data sets: Application of noisy-or gates International Journal of Approximate Reasoning 27 2001 165 182
73. L. Ortiz, and L. Kaelbling Sampling methods for action selection in influence diagrams H. Kautz, B. Porter, Proceedings of the 17th National Conference on Artificial Intelligence 2000 AAAI Press Menlo Park, CA 378 385
74. D. Owens, R. Shachter, and R. Nease Representation and analysis of medical decision problems with influence diagrams Medical Decision Making 17 1997 241 262
75. S. Pauker, and J. Wong The influence of influence diagrams in medicine Decision Analysis 2 2005 238 244
76. J. Pearl Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference 1988 Morgan Kaufmann
77. J. Pearl Influence diagrams-Historical and personal perspectives Decision Analysis 2 2005 232 234
78. M. Pradhan, G. Provan, B. Middleton, and M. Henrion Knowledge engineering for large belief networks R. Mántaras, D. Poole, Uncertainty in Artificial Intelligence: Proceedings of the 10th Conference 1994 Morgan Kaufmann San Francisco, CA 484 490
79. C. Pralet, G. Verfaillie, T. Schiex, Composite graphical models for reasoning about uncertainties, feasibilities and utilities, in: Proceedings of the 7th International CP-05 Workshop on Preferences and Soft Constraints, pp. 104-118.
80. M. Ramoni Anytime influence diagrams, in: Working Notes of the IJCAI-95 Workshop on Anytime Algorithms and Deliberation Scheduling 1995 AAAI Press Menlo Park, CA pp. 55-62
81. T. Saati The Analytic Hierarchy Process 1980 McGraw-Hill
82. U. Schmidt A measurement of the certainty effect Journal of Mathematical Psychology 42 1998 32 47
83. R. Shachter Evaluating influence diagrams Operations Research 34 1986 871 882
84. R. Shachter Efficient value of information computation K. Laskey, H. Prade, Uncertainty in Artificial Intelligence: Proceedings of the 15th Conference 1999 Morgan Kaufmann San Francisco, CA 594 602
85. R. Shachter, and P. Ndilikilikesha Using potential influence diagrams for probabilistic inference and decision making D. Heckerman, A. Mamdani, Uncertainty in Artificial Intelligence: Proceedings of the 15th Conference 1993 Morgan Kaufmann San Francisco, CA 383 390
86. P. Shenoy Valuation-based systems for Bayesian decision analysis Operations Research 40 1992 463 484
87. S. Srinivas A generalization of the noisy-OR model D. Heckerman, A. Mamdani, Uncertainty in Artificial Intelligence: Proceedings of the 9th Conference 1993 Morgan Kaufmann San Francisco, CA 208 215
88. D. Suryadi, and P. Gmytrasiewicz Learning models of other agents using influence diagrams J. Kay, Proceedings of the 7th International Conference on User Modeling 1999 Springer-Verlag New York, NY 223 232
89. J. Tatman, and R. Shachter Dynamic programming and influence diagrams IEEE Transactions on Systems, Man and Cybernetics 20 1990 365 379
90. D. Timmermans The roles of experience and domain of expertise in using numerical and verbal probability terms in medical decisions Medical Decision Making 14 1994 146 156
91. H. Wang, and M. Druzdzel User interface tools for navigation in conditional probability tables and elicitation of probabilities in Bayesian networks C. Boutilier, M. Goldszmidt, Uncertainty in Artificial Intelligence: Proceedings of the 16th Conference 2000 Morgan Kaufmann San Francisco, CA 617 625
92. N. Wilson Extending CP-nets with stronger conditional preference statements G. Ferguson, D. McGuinness, Proceedings of the 19th National Conference on Artificial Intelligence 2004 AAAI Press Menlo Park, CA 735 741
93. Y. Xiang, and K. Poh Time-critical dynamic decision making K. Laskey, H. Prade, Uncertainty in Artificial Intelligence: Proceedings of the 15th Conference 1999 Morgan Kaufmann San Francisco, CA 688 695
94. N. Zhang Probabilistic inference in influence diagrams G. Cooper, S. Moral, Uncertainty in Artificial Intelligence: Proceedings of the 14th Conference 1998 Morgan Kaufmann San Francisco, CA 514 522
95. N. Zhang Probabilistic inference in influence diagrams G. Cooper, S. Moral, Uncertainty in Artificial Intelligence: Proceedings of the 14th Conference 1998 Morgan Kaufmann San Francisco, CA 514 522