{"title":"Qualitative Possibilistic Influence Diagrams","authors":"Wided GuezGuez, Nahla Ben Amor, Khaled Mellouli","volume":21,"journal":"International Journal of Mathematical and Computational Sciences","pagesStart":671,"pagesEnd":677,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/1815","abstract":"
Influence diagrams (IDs) are one of the most commonly used graphical decision models for reasoning under uncertainty. The quantification of IDs which consists in defining conditional probabilities for chance nodes and utility functions for value nodes is not always obvious. In fact, decision makers cannot always provide exact numerical values and in some cases, it is more easier for them to specify qualitative preference orders. This work proposes an adaptation of standard IDs to the qualitative framework based on possibility theory.<\/p>\r\n","references":"[1] A. Akari, microeconomie du consommateur et du producteur,\r\npages 35:95, 2000.\r\n[2] N. Ben Amor, S.Benferhat and K.Mellouli, Anytime propagation\r\nalgorithm for min-based possibilistic graphs, Soft\r\nComputing a fusion of foundations methodologies and\r\napplications, Springer Verlag, Vol 8, pages 150:161, 2001.\r\n[3] N. Ben Amor. Qualitative Possibilistic Graphical Models:\r\nFrom Independence to Propagation Algorithms. Ph.D.\r\ndissertation, 2002.\r\n[4] G. F. Cooper, A method for using belief networks as\r\ninfluence diagrams, Fourth workshop on uncertainty in\r\narti\u252c\u00bbcial intelligence, 1988.\r\n[5] D. Dubois and H. Prade, Possibility theory, an approach\r\nto computerized processing of uncertainty, Plenum Press,\r\nNew York, NY, 1988.\r\n[6] D. Dubois and .H Prade,Didier Dubois and Henri Prade,\r\nAn introductory survey of possibility theory and its recent\r\ndevelopments, 1998.\r\n[7] D. Dubois, H. Prade and P. Smets, New semantics for\r\nquantitative possibility theory, , 2nd International Symposium\r\non imprecise probabilities and their applications,\r\nIthaca, new York, 2001.\r\n[8] R.A. Howard and J.E. Matheson, Influence diagrams. In\r\nthe principles and applications of decision analysis, Vol II,\r\nR.A Howard and J.E Matheson (eds). Strategic decisions\r\ngroup, Menlo Park, Calif, 1984.\r\n[9] F.V. Jensen, Introduction to Bayesian networks, UCL\r\nPress, 1996.\r\n[10] F.V. Jensen, Bayesian networks and decision graphs.\r\nSpringer, statistics for engineering and information science,\r\n2002.\r\n[11] J. Kim and J. Pearl, Convince, A conversational inference\r\nconsolidation engine, IEEE Trains. on Systems, Man and\r\nCybernetics 17, pages 120:132, 1987.\r\n[12] J.Pearl, Causality Models, Reasoning and Inference, Cambridge\r\nUniversity Press, 2000.\r\n[13] P.P. Shenoy , A comparison of graphical techniques for\r\ndecision analysis, European Journal of Operational Research,\r\nVol 78, pages 1:21, 1994.\r\n[14] P. H. Giang, P.P. Shenoy, Two axiomatic approaches to\r\ndecision making using possibility theory, European journal\r\nof operational research, Vol 162 No.2, pages 450:467, 2005.\r\n[15] R. D. Shachter, Evaluating influence diagrams, Operation\r\nResearch 34 pages 871:882, 1986.\r\n[16] R. D. Shachter and M. A. Poet, Decision making using\r\nprobabilistic inference methods,In Proceedings of 8th\r\nConference on Uncertainty in Arti\u252c\u00bbcial Intelligence, pages\r\n276:283, 1992.\r\n[17] J. Von Neumann and O.Morgenstern, theory of games and\r\neconomic behavior, Princeton University Press, 1948.\r\n[18] N. L. Zhang, Probabilistic inference in influence diagrams,\r\nIn Proceedings of 14th Conference on Uncertainty in\r\nArtificial Intelligence, pages 514-522, 1998.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 21, 2008"}