Representing and Solving Decision Problems with Limited Information

References

• Cooper G. F.A method for using belief networks as influence diagrams (1998) Proc. 4th Workshop Uncertainty Artificial Intelligence(Minneapolis, MN)55–63Google Scholar
• Cowell R. G., Dawid A. P., Lauritzen S. L., Spiegelhalter D. J.Probabilistic Networks and Expert Systems (1999) (Springer-Verlag, New York) Google Scholar
• Fagiuoli E., Zaffalon M. A note about redundancy in influence diagrams. Inter. J. Approximate Reasoning (1998) 19:351–365Crossref, Google Scholar
• Howard R. A.Dynamic Programming and Markov Processes (1960) (MIT Press, Cambridge, MA) Google Scholar
• Howard R. A., Matheson J. E., Howard R. A., Matheson J. E. Influence diagrams. Readings in the Principles and Applications of Decision Analysis (1984) (Strategic Decisions Group, Menlo Park, CA) Google Scholar
• Jensen F., Jensen F. V., Dittmer S. L., de Mantaras R. L., Poole D.From influence diagrams to junction trees (1994) Proc. 10th Conf. Uncertainty Artificial Intelligence(Morgan Kaufmann Publishers, San Francisco, CA) 367–373Crossref, Google Scholar
• Kjærulff U. Optimal decomposition of probabilistic networks by simulated annealing. Statist. Comput. (1992) 2:7–17Crossref, Google Scholar
• Lovejoy W. S. A survey of algorithmic methods for partially observed Markov decision processes. Ann. Oper. Res. (1991) 28:47–66Crossref, Google Scholar
• Madsen A. L., Jensen F. V., Cooper G. F., Moral S.Lazy propagation in junction trees (1998) Proc. 14th Ann. Conf. Uncertainty Artificial Intelligence(Morgan Kaufmann Publishers, San Francisco, CA) 362–369Google Scholar
• Nielsen T. D., Jensen F. V., Laskey K., Prade H.Well-defined decision scenarios (1999) Proc. 15th Ann. Conf. Uncertainty Artificial Intelligence(Morgan Kaufmann Publishers, San Francisco, CA) 502–511Google Scholar
• Nilsson D., Lauritzen S. L., Boutilier C., Goldszmidt M.Evaluating influence diagrams using LIMIDs (2000) Proc. 16th Conf. Uncertainty Artificial Intelligence(Morgan Kaufmann Publishers, San Francisco, CA) 436–445Google Scholar
• Olmsted S. M.On representing and solving decision problems (1983) (Department of Engineering-Economic Systems, Stanford University, Stanford, CA) . Ph. D. ThesisGoogle Scholar
• Pearl J., Kanal L. N., Lemmer J. F. A constraint-propagation approach to probabilistic reasoning. Uncertainty Artificial Intelligence (1986) (North-Holland, Amsterdam, The Netherlands)357–370Crossref, Google Scholar
• Shachter R., Laskey K., Prade H.Efficient value of information computation (1999) Proc. 15th Ann. Conf. Uncertainty Artificial Intelligence(Morgan Kaufmann Publishers, San Francisco, CA) 594–601Google Scholar
• Shachter R. Evaluating influence diagrams. Oper. Res. (1986) 34:871–882Link, Google Scholar
• Shenoy P. P. Valuation-based systems for Bayesian decision analysis. Oper. Res. (1992) 40:463–484Link, Google Scholar
• Shenoy P. P., Shafer G. R., Shachter R. D., Levitt T. S., Kanal L. N., Lemmer J. F. Axioms for probability and belief-function propagation. Uncertainty Artificial Intelligence 4 (1990) (North-Holland, Amsterdam, The Netherlands)169–198Crossref, Google Scholar
• Verma T., Pearl J., Shachter R. D., Levitt T. S., Kanal L. N., Lemmer J. F. Causal networks: Semantics and expressiveness. Uncertainty Artificial Intelligence 4 (1990) (North-Holland, Amsterdam, The Netherlands)69–76Crossref, Google Scholar
• White C. C. A survey of solution techniques for the partially observed Markov decision process. Ann. Oper. Res. (1991) 32:215–230Crossref, Google Scholar
• Zhang N. L., Qi R., Poole D. A computational theory of decision networks. Inter. J. Approximate Reasoning (1994) 11:83–158Crossref, Google Scholar