Abstract
A significant class of decision making problems consists of choosing actions, to be carried out simultaneously, in order to achieve a trade-off between different objectives. When such decisions concern complex systems, decision support tools including formal methods of reasoning and probabilistic models are of noteworthy helpfulness. These models are often built through learning procedures, based on an available knowledge base. Nevertheless, in many fields of application (e.g. when dealing with complex political, economic and social systems), it is frequently not possible to determine the model automatically, and this must then largely be derived from the opinions and value judgements expressed by domain experts. The BayMODE decision support tool (Bayesian Multi Objective Decision Environment), which we describe in this paper, operates precisely in such contexts. The principal component of the program is a multi-objective Decision Network, where actions are executed simultaneously. If the noisy-OR assumptions are applicable, such a the model has a reasonably small number of parameters, even when actions are represented as non-binary variables. This makes the model building procedure accessible and easy. Moreover, BayMODE operates with a multi-objective approach, which provides the decision maker with a set of non-dominated solutions, computed using a multi-objective genetic algorithm.
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References
Druzdzel MJ, Flynn RR (2000) Decision support systems. In: Encyclopedia of library and information science, vol 67. Marcel Dekker, Inc., New York, pp 120–133
von Neumann J, Morgenstern O (1944) The theory of games and economic behavior. Princeton University Press, Princeton
Pearl J (1988) Probabilistic reasoning in intelligent systems. Morgan Kaufman, San Mateo, CA
Korb, KB, Nicholson AE (2004) Bayesian artificial intelligence. CRC/Chapman and Hall, Boca Raton
Murphy K (2006) Software packages for graphical models/Bayesian networks. http://www.cs.ubc.ca/murphyk/Software/bnsoft.html
Morgan MG, Henrion M (1990) Uncertainty: a guide to dealing with uncertainty in quantitative risk and policy analysis. Cambridge University Press, New York
Druzdzel MJ (1999) SMILE: structural modeling, inference, and learning engine and GeNIE: a development environment for graphical decision-theoretic models. In: Proceedings of the sixteenth national conference on artificial intelligence and eleventh conference on innovative applications of artificial intelligence, AAAI Press/The MIT Press, Orlando, Florida, USA. 1999, pp 902–903
Madsen AL, Jensen F, Kjærulff U, Lang M (2005) The Hugin tool for probabilistic graphical models. Int J Artif Intell Tools 14(3):507–544
Haddawy P, Jacobson J, Kahn CE Jr (1997) BANTER: a Bayesian network tutoring shell. Artif Intell Med 10(2):177–200
Milho I, Fred ALN (2000) A user-friendly development tool for medical diagnosis based on Bayesian networks. In: Proceedings of the second international conference on enterprise information systems Stafford, UK, pp 176–180
Howard R, Matheson J (1984) Influence diagrams. In: Howard R, Matheson J (eds), The principles and applications of decision analysis. Strategic Decisions Group, Menlo Park, CA, pp 719–762
Zagorecki A, Druzdzel MJ (2004) An empirical study of probability elicitation under noisy-OR assumption. In Proceedings of the seventeenth internationalFlorida artificial intelligence research society conference, 2004
Diehl M, Haimes YY (2004) Influence diagrams with multiple objectives and tradeoff analysis. IEEE Trans Syst Man Cybern—A 34(3):293–304
Pareto V (1896) Cours d’Economie politique, vol I, II. F. Rouge, Lausanne
Henrion M (1989) Uncertainty in artificial intelligence3, ch. Some practical issues in constructing belief networks. Elsevier Science Publishing Company, Inc., pp 161–173
Díez FJ (1993) Parameter adjustment in Bayes networks. The generalized noisy-OR gate. In: Proceedings of the ninth annual conferenceon Uncertainty in Artificial Intelligence. Morgan Kaufmann, pp 99–105
Zhang W, Ji Q (2006) A factorization approach to evaluating simultaneous influence diagrams. IEEE Trans Syst, Man Cybern—A 36(4):746–754
Keeney R, Raiffa H (1976) Decision with multiple objectives: preferences and value trade-offs. John Wiley & Sons, Inc., New York
Evans GE (1984) An overview of techniques for solving multiobjective mathematical programs. Manage Sci 30(11):1268–1282
Zeleny M (1982) Multiple criteria decision making. McGraw-Hill, New York
Haimes YY, Hall WA (1974) Multiobjectives in water resources systems analysis: the surrogate worth trade-off method. Water Resour Res 10(4):615–624
Pearl J (2000) Causality. Cambdrige University Press, New York, NY
Shachter RD (1986) Evaluating influence diagrams. Oper Res 34(6):871–882
Bellman R (1957) Dynamic programming. Princeton University Press, Princeton
Cooper G (1988) A method for using belief networks as influence diagrams. In: Procceedings of the twelfth conference on uncertainty in artificial intelligence. Morgan Kaufmann, pp 55–63
Shachter RD, Peot MA (1992) Decision making using probabilistic inference methods. In: Proceedings of the eighth annual conference on uncertainty in artificial intelligence. Morgan Kaufmann, pp 276–283
Zhang NL (1998) Probabilistic inference in influence diagrams. In: Proceedings of the fourteenth conference on uncertainty in artificial intelligence. Morgan Kaufmann, pp 514–522
Shenoy P (1992) Valuation-based systems for Bayesian decision analysis. Oper Res 40(3):463–484
Jensen F, Jensen FV, Dittmer SL (1994) From influence diagrams to junction trees. In: Proceedings of the tenth annual conference on uncertainty in artificial intelligence. Morgan Kaufmann, pp 367–373
Zhang NL, Qi R, Poole D (1994) A computational theory of decision networks. Int J Approx Reasoning 11(2):83–158
Jensen FV, Vomlelová M (2002) Unconstrained influence diagrams. In: Proceedings of the 18th conference in uncertainty in artificial intelligence. Morgan Kaufmann, pp 234–241
Nielsen TD, Jensen FV (1999) Welldefined decision scenarios. In: Proceedings of the fifteenth conference on uncertainty in artificial intelligence. Morgan Kaufmann, pp 502–511
Rojas-Guzmán C, Kramer MA (1993) GALGO: A Genetic ALGOrithm decision support tool for complex uncertain systems modeled with Bayesian belief networks. In: Proceedings of the ninth annual conference on uncertainty in artificial intelligence. Morgan Kaufmann, pp 368–375
Rojas-Guzmán C, Kramer MA (1996) An evolutionary computing approach to probabilistic reasoning on Bayesian networks. Evol Comput 4(1):57–85
Gelsema ES (1995) Abductive reasoning in Bayesian belief networks using a genetic algorithm. Pattern Recognit Lett 16(8):865–871
Gelsema ES (1996) Diagnostic reasoning based on a genetic algorithm operating in a Bayesian belief network. Pattern Recognit Lett 17(10):1047–1055
de Campos LM, Gámez JA, Moral S (1999) Partial abductive inference in Bayesian belief networks using a genetic algorithm. Pattern Recognit Lett 20(11–13):1211–1217
de Campos LM, Gámez JA, Moral S (2002) Partial abductive inference in Bayesian belief networks—an evolutionary computation approach by using problem-specific genetic operators. IEEE Trans Evol Comput 6(2):105–131
Coello CC, Veldhuizen DV, Lamont G (2002) Evolutionary algorithms for solving multi-objective problems. Kluwer Academic Publishers
Fonseca C, Fleming P (1995) An overview of evolutionary algorithms in multiobjective optimization. Evol Comput 3(1):1– 16
Deb K, Agrawal S, Pratap A, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197
Jensen MT (2003) Reducing the run-time complexity of multiobjective EAs: The NSGA-II and other algorithms. IEEE Trans Evol Comput 7(5):503–515
Nojima Y, Narukawa K, Kaige S, Ishibuchi H (2005) Effects of removing overlapping solutions on the performance of the NSGA-II algorithm. In: Evolutionary multi-criterion optimization, third international conference, proceedings, vol 3410 of Lecture Notes in Computer Science, Springer, 2005, pp 341–354
Drzadzewski G, Wineberg M (2005) A comparison between dynamic weighted aggregation and NSGA-II for multi-objective evolutionary algorithms. In: Computational intelligence (IASTED international conference on computational intelligence, IASTED/ACTA Press, pp 327–331
Goldberg D (1998) Genetic algorithms and evolution strategy in engineering and computer science: Recent advances and industrial applications. Wiley.
Fung RM, Chang K-C (1989) Weighing and integrating evidence for stochastic simulation in Bayesian networks. In: Proceedings of the fifth annual conference on uncertainty in artificial intelligence, Morgan Kaufmann, 1989, pp 209–220
Shachter RD, Peot MA (1989) Simulation approaches to general probabilistic inference on belief networks. In: Proceedings of the fifth annual conference on uncertainty in artificial intelligence, Morgan Kaufmann, 1989, pp 221–234
Henrion M (1986) Propagating uncertainty in Bayesian networks by probabilistic logic sampling. In: Proceedings of the second annual conference on uncertainty in artificial intelligence. Morgan Kaufmann, 1986, pp 149–164
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Ivan Blecic is Assistant Professor of Economic Appraisal and Evaluation at the Faculty of Architecture in Alghero (University of Sassari, Italy) and member of Interuniversity Laboratory of Analysis and Models for Planning (LAMP). He received a Ph.D. in Planning and Public Policies in 2005 from IUAV University of Venice where he has also been a research fellow at the Department of Planning. His current research interests include analysis and modelling for planning, evaluation techniques and modelling, decision support systems and methods for public participation.
Arnaldo Cecchini graduated cum laude in Physics at the University of Bologna in 1972. He is Professor of Analysis of Urban Systems at the Faculty of Architecture in Alghero (University of Sassari), Director of the Urban and Environmental Planning Course, Vice-Dean of the Faculty of Architecture in Alghero and Director of the Interuniversity Laboratory of Analysis and Models for Planning - LAMP. He is the author of more than 100 articles and papers published in books and refereed journals and is an expert in techniques of urban analysis and for public participation: simulation, gaming simulation, cellular automata, scenario techniques.
Giuseppe A. Trunfio gained a Ph.D. in Computational Mechanics in 1999 at the University of Calabria, Italy. He has been a research fellow at the Italian National Research Council where he has worked extensively on the application of parallel computing to the simulation of complex systems. He is Assistant Professor of Computer Engineering at the Department of Architecture and Planning of the University of Sassari and his current research interests include decision support, probabilistic models, neural networks, evolutionary computation and cellular automata.
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Blecic, I., Cecchini, A. & Trunfio, G.A. A decision support tool coupling a causal model and a multi-objective genetic algorithm. Appl Intell 26, 125–137 (2007). https://doi.org/10.1007/s10489-006-0009-z
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DOI: https://doi.org/10.1007/s10489-006-0009-z