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From knowledge bases to decision models

Published online by Cambridge University Press:  07 July 2009

Michael P. Wellman ,
John S. Breese  and
Robert P. Goldman
Michael P. Wellman
Affiliation:
USAF Wright Laboratory, Wright-Patterson AFB, OH45433, USA
John S. Breese
Affiliation:
Rockwell International Science Center, Palo Alto, CA 94301, USA
Robert P. Goldman
Affiliation:
Tulane University, New Orleans, LA 70118, USA
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Abstract

In recent years there has been a growing interest among AI researchers in probabilistic and decision modelling, spurred by significant advances in representation and computation with network modelling formalisms. In applying these techniques to decision support tasks, fixed network models have proven to be inadequately expressive when a broad range of situations must be handled. Hence many researchers have sought to combine the strengths of flexible knowledge representation languages with the normative status and well-understood computational properties of decision-modelling formalisms and algorithms. One approach is to encode general knowledge in an expressive language, then dynamically construct a decision model for each particular situation or problem instance. We have developed several systems adopting this approach, which illustrate a variety of interesting techniques and design issues.

Type
Research Article
Information
The Knowledge Engineering Review , Volume 7 , Issue 1 , March 1992 , pp. 35 - 53
Copyright
Copyright © Cambridge University Press 1992

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References

Bacchus, F, 1990. Representing and Reasoning with Probabilistic Knowledge: A Logical Approach to Probabilities MIT Press.Google Scholar
Breese, JS and Horvitz, EJ, 1990. “Ideal reformulation of belief networks” in: Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence,Cambridge, MA,July, 64–72.Google Scholar
Breese, JS, 1990. Construction of belief and decision networks, Technical Memorandum 30, Rockwell International Science Center, Palo Alto, CA, January. To appear in Computational Intelligence.Google Scholar
Buede, DM, 1986. “Structuring value attributes” Interfaces 16(2) 52–62.CrossRefGoogle Scholar
Charniak, E and McDermott, D, 1985. Introduction to Artificial Intelligence Addison-Wesley.Google Scholar
Clark, DA, Fox, J, Glowinski, AJ and O'Neil, MJ, 1990. “Symbolic reasoning for decision making” in: Borcherding, K, Larichev, OI and Messick, DM eds., Contemporary Issues in Decision Making Elsevier Science Publishers.Google Scholar
Cooper, GF and Herskovits, E, 1991. “A Bayesian method for constructing Bayesian belief networks from databases” in: Proceedings of the Seventh Conference on Uncertainty in Artificial Intelligence,Los Angeles, CA, 86–94.CrossRefGoogle Scholar
D'Ambrosio, B and Fehling, M, 1989. “Resource-bounded agents in an uncertain world” in: AAAI Spring Symposium on Artificial Intelligence and Limited Rationality, 13–17.Google Scholar
Dean, T and Boddy, M, 1988. “An analysis of time-dependent planning” in: Proceedings of the National Conference on Artificial Intelligence, 49–54.Google Scholar
Fertig, KW and Breese, JS, 1989. “Interval influence diagrams” in: Proceedings of the Workshop on Uncertainty in Artificial Intelligence, Windsor, ON, 102–111.Google Scholar
Fox, J, 1991. “Decision theory and autonomous systems” in: Singh, M and Travé-Massuyés, L, eds., Decision Support Systems and Qualitative Reasoning North-Holland.Google Scholar
Geiger, D, Paz, A and Pearl, J, 1990. “Learning causal trees from dependence information” in: Proceedings of the National Conference on Artificial Intelligence,Boston, MA, 770–776.Google Scholar
Geman, S and Geman, D, 1984. “Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images” IEEE Transactions on Pattern Analysis and Machine Intelligence 6 721–741.CrossRefGoogle ScholarPubMed
Goldman, RP and Charniak, E, 1990. “Dynamic construction of belief networks” in: Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence,Cambridge, MA, 90–97.Google Scholar
Goldman, RP, 1990. “A probabilistic approach to language understanding” Technical Report CS–90–34, Brown University Department of Computer Science, 12.Google Scholar
Haddawy, P and Hanks, S, 1990. “Issues in decision-theoretic planning: Symbolic goals and numeric utilities” in: Proceedings of the DARPA Workshop on Innovative Approaches to Planning, Scheduling, and Control, 48–58.Google Scholar
Haddawy, P and Rendell, L, 1990. “Planning and decision theory”, Knowledge Engineering Review 5 15–33.CrossRefGoogle Scholar
Haddawy, P, 1991. “A temporal probability logic for representing actions” in: Principles of Knowledge Representation and Reasoning: Proceedings of the Second International Conference, 313–324.Google Scholar
Halpern, JY, 1990. “An analysis of first-order logics of probability” Artificial Intelligence 46 311–350.CrossRefGoogle Scholar
Hanks, SJ, 1990. “Projecting plans for uncertain worlds” Technical Report YALEU/CSD/RR 756, Yale University Department of Computer Science, 01.Google Scholar
Hansson, O, Mayer, A and Russell, S, 1990. “Decision-theoretic planning in BPS” in: AAAI Symposium on Planning in Uncertain, Unpredictable, or Changing Environments (Available as report 90–45, University of Maryland Systems Research Center).Google Scholar
Heckerman, DE, Horvitz, EJ and Nathwani, BN, “Toward normative expert systems: The Pathfinder project” Methods of Information in Medicine (to appear).Google Scholar
Henrion, M and Druzdzel, MJ, 1990. “Qualitative propagation and scenario-based approaches to explanation of probabilistic reasoning” in: Proceedings of the Sixth Conference on Uncertainty in Artificial intelligence,Cambridge, MA, 10–20.Google Scholar
Hobbs, JR, Stickel, M, Martin, P and Edwards, D, 1988. “Interpretation as abduction” in: Proceedings of the 26th Annual Meeting of the ACL, 95–103.CrossRefGoogle Scholar
Holtzman, S, 1989. Intelligent Decision Systems Addison-Wesley.CrossRefGoogle Scholar
Horvitz, EJ, Breese, JS and Henrion, M, 1988. “Decision theory in expert systems and artificial intelligence” International journal of Approximate Reasoning 2 247–302.CrossRefGoogle Scholar
Horvitz, EJ, 1988. Reasoning under varying and uncertain resource constraints” in: Proceedings of the National Conference on Artificial Intelligence, 111–116.Google Scholar
Howard, RA and Matheson, JE, 1984b. “Influence diagrams” in: The Principles and Applications of Decision Analysis, 719–762.Google Scholar
Howard, RA and Matheson, JE, eds., 1984. The Principles and Applications of Decision Analysis Strategic Decisions Group.Google Scholar
Kanazawa, K, 1991. “A logic and time nets for probabilistic inference” in: Proceedings of the National Conference on Artificial Intelligence,Anaheim, CA, 360–365.Google Scholar
Keeney, RL and Raiffa, H, 1976. Decisions with Multiple Objectives: Preferences and Value Tradeoffs John Wiley.Google Scholar
Keeney, RL, 1986. “Identifying and structuring values” Decision analysis series report, University of Southern California, Los Angeles, CA, 12.Google Scholar
Langlotz, CP, Shortliffe, EH and Fagan, LM, 1988. “A methodology for generating computer-based explanations of decision-theoretic advice” Medical Decision Making 8 290–303.CrossRefGoogle ScholarPubMed
Laskey, KB, 1990. “A probabilistic reasoning environment” in: Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence,Cambridge, MA, 415–422.Google Scholar
Laskey, KB, 1991. “Conflict and surprise: Heuristics for model revision” in: Proceedings of the Seventh Conference on Uncertainty in Artificial Intelligence,Los Angeles, CA, 197–204.CrossRefGoogle Scholar
Lehner, PE and Adelman, L, 1990. “Behavioural decision theory and its implication for knowledge engineering” Knowledge Engineering Review 5 5–14.CrossRefGoogle Scholar
Leong, TY, 1991. “Knowledge representation for supporting decision model formulation in medicine” Technical Report 504, MIT Laboratory for Computer Science, Cambridge, MA, 05.Google Scholar
Leong, TY, 1991. “Representation requirements for supporting decision model formulation” in: Proceedings of the Seventh Conference on Uncertainty in Artificial Intelligence,Los Angeles, CA, 212–219.CrossRefGoogle Scholar
Levitt, TS, Binford, TO and Ettinger, GJ, 1990. “Utility based control for computer vision” in: Shachter, RD, Levitt, TS, Lemmer, JF and Kanal, LN eds., Uncertainty in Artificial Intelligence 4, 407–422Elsevier Science.CrossRefGoogle Scholar
Loui, RP, 1989. “Defeasible decisions: What the proposal is and isn't” in: Proceedings of the Workshop on Uncertainty in Artificial Intelligence, Windsor, ON, 245–252.Google Scholar
Nayak, PP, Joskowicz, L and Addanki, S, 1991. “Automated model selection using context-dependent behaviors” in: Fifth International Workshop on Qualitative Physics,Austin, TX,May.Google Scholar
Neapolitan, RE, 1990. Probabilistic Reasoning in Expert Systems: Theory and Algorithms John Wiley.Google Scholar
Pearl, J, Geiger, D and Verma, T, 1989. “Conditional independence and its representations” Kybernetika 25 33–44.Google Scholar
Pearl, J, 1988. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference Morgan Kaufmann.Google Scholar
Pereira, FCN and Warren, DHD, 1980. “Definite clause grammars for language analysis—A survey of the formalism and comparison with augmented transition networks” Artificial Intelligence 13 231–278.CrossRefGoogle Scholar
Poole, D, 1991. “Representing Bayesian networks within probabilistic Horn abduction” in: Proceedings of the Seventh Conference on Uncertainty in Artificial Intelligence,Los Angeles, CA, 271–278.CrossRefGoogle Scholar
Provan, GMA, 1991. “Dynamic network updating techniques for diagnostic reasoning” in: Proceedings of the Seventh Conference on Uncertainty in Artificial Intelligence,Los Angeles, CA, 279–286.CrossRefGoogle Scholar
Raiffa, H, 1968. Decision Analysis: Introductory Lectures on Choices Under Uncertainty Addison-Wesleyxs.Google Scholar
Reiter, R, 1987. “A theory of diagnosis from first principles” Artificial Intelligence 32 57–96.CrossRefGoogle Scholar
Russell, S and Wefald, E, 1991. Do the Right Thing: Studies in Limited Rationality MIT Press.Google Scholar
Saffiotti, A, 1990. “A hybrid framework for representing uncertain knowledge” in: Proceedings of the National Conference on Artificial Intelligence,Boston, MA, 653–658.Google Scholar
Savage, LJ, 1972. The Foundations of Statistics Dover Publications.Google Scholar
Shachter, RD, 1986. “Evaluating influence diagrams” Operations Research 34 871–882.CrossRefGoogle Scholar
Shachter, RD, 1988. “Probabilistic inference and influence diagrams” Operations Research 36 589–604.CrossRefGoogle Scholar
Smith, DE, 1988. “A decision-theoretic approach to the control of planning search” Technical Report LOGIC-87–11, Department of Computer Science, Stanford University, 01.Google Scholar
Vilain, MB, 1985. “The restricted language architecture of a hybrid representation system” in: Proceedings of the Ninth International Joint Conference on Artificial Intelligence, 547–551.Google Scholar
Weld, DS and de Kleer, J, eds., 1989. Readings in Qualitative Reasoning About Physical Systems Morgan Kaufmann.Google Scholar
Weld, DS, 1991. “Reasoning about model accuracy” Technical Report 91–05–02, Department of Computer Science and Engineering, University of Washington, 06.Google Scholar
Wellman, MP and Doyle, J, 1991. “Preferential semantics for goals” in: Proceedings of the National Conference on Artificial Intelligence,Anaheim, CA, 698–703.Google Scholar
Wellman, MP and Henrion, M, 1991. “Qualitative intercausal relations, or Explaining ‘explaining away’” in: Principles of Knowledge Representation and Reasoning: Proceedings of the Second International Conference, 535–546.Google Scholar
Wellman, MP, Eckman, MH, Fleming, C, Marshall, SL, Sonnenberg, FA and Pauker, SG, 1989. “Automated critiquing of medical decision trees” Medical Decision Making 9 272–284.CrossRefGoogle ScholarPubMed
Wellman, MP, 1986. “Representing health outcomes for automated decision formulation” in: Salamon, R, Blum, B and Jørgensen, M, eds., MEDINFO 86: Proceedings of the Fifth Conference on Medical Informatics, 789–793, Washington,October.Google Scholar
Wellman, MP, 1990. Formulation of Tradeoffs in Planning Under Uncertainty Pitman.Google Scholar
Wellman, MP, 1990. “Fundamental concepts of qualitative probabilistic networks” Artificial Intelligence 44 257–303.CrossRefGoogle Scholar
Yen, J and Bonissone, PP, 1990. “Extending term subsumption systems for uncertainty management” in: Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence,Cambridge, MA, 468–473.Google Scholar