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Simon Parsons, "Current Approaches to Handling Imperfect Information in Data and Knowledge Bases," IEEE Transactions on Knowledge and Data Engineering, vol. 8, no. 3, pp. 353372, June, 1996.  
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@article{ 10.1109/69.506705, author = {Simon Parsons}, title = {Current Approaches to Handling Imperfect Information in Data and Knowledge Bases}, journal ={IEEE Transactions on Knowledge and Data Engineering}, volume = {8}, number = {3}, issn = {10414347}, year = {1996}, pages = {353372}, doi = {http://doi.ieeecomputersociety.org/10.1109/69.506705}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Knowledge and Data Engineering TI  Current Approaches to Handling Imperfect Information in Data and Knowledge Bases IS  3 SN  10414347 SP353 EP372 EPD  353372 A1  Simon Parsons, PY  1996 KW  Imperfect information KW  uncertainty KW  databases KW  artificial intelligence KW  knowledge representation KW  reasoning. VL  8 JA  IEEE Transactions on Knowledge and Data Engineering ER   
Abstract—This paper surveys methods for representing and reasoning with imperfect information. It opens with an attempt to classify the different types of imperfection that may pervade data, and a discussion of the sources of such imperfections. The classification is then used as a framework for considering work that explicitly concerns the representation of imperfect information, and related work on how imperfect information may be used as a basis for reasoning. The work that is surveyed is drawn from both the field of databases and the field of artificial intelligence. Both of these areas have long been concerned with the problems caused by imperfect information, and this paper stresses the relationships between the approaches developed in each.
[1] S. Abiteboul,P. Kanellakis,, and G. Grahne,“On the representation and querying of sets of possible worlds,” Proc. ACM SIGMOD Conf., pp. 3448, 1987.
[2] H. Koike, T. Takeshima, and M. Takada, "A BIST Scheme Using Micro Program ROM for Large Capacity Memories," Proc. IEEE Int'l Test Conf., pp. 815822, 1990.
[3] A. AlZobaidie and J.B. Grimson, "Expert Systems and Database Systems: How Can They Serve Each Other?," Expert Systems, vol. 4, pp. 3037, 1987.
[4] S. Amarger, D. Dubois, and H. Prade, "Constraint Propagation with Imprecise Conditional Probabilities," Proc. Seventh Conf. Uncertainty in Artificial Intelligence, pp. 2634,Los Angeles, 1991.
[5] S.K. Andersen, K.G. Olesen, F.V. Jensen, and F. Jensen, "HUGIN—A Shell for Building Belief Universes for Expert Systems," Proc. 11th Int'l Joint Conf. Artificial Intelligence, pp. 1,0801,085, Sydney, 1989.
[6] S. Andreassen, M. Woldbye, B. Falck, and S.K. Andersen, "MUNIN—A Causal Probabilistic Network for Interpretation of Electromyographic Findings," Proc. 10th Int'l Joint Conf. Artificial Intelligence, pp. 366372 Milan, 1987.
[7] F. Bacchus, "On Probability Distributions Over Possible Worlds," Uncertainty in Artificial Intelligence 4, R.D. Shacter, T.S. Levitt, L.N. Kanal, and J.F. Lemmer, eds. Elsevier Science, 1990.
[8] J.F. Baldwin, "Evidential Support Logic Programming," Fuzzy Sets and Systems, vol. 24, pp. 126, 1987.
[9] J.F. Baldwin, "Automated Fuzzy and Probabilistic Inference," Fuzzy Sets and Systems, vol. 18, pp. 219235, 1986.
[10] J.F. Baldwin and S.Q. Zhou, "A Fuzzy Relational Inference Language," Fuzzy Sets and Systems, vol. 14, pp. 155174, 1984.
[11] D. Barbara, H. GarciaMolina, and D. Porter, "A Probabilistic Relational Data Model," Proc. 1990 EDBT Conf., pp. 6074., 1990.
[12] S. Beneferhat, D. Dubois, and H. Prade, "Representing Default Rules in Possibilistic Logic," Proc. Third Int'l Conf. Knowledge Representation and Reasoning, pp. 673684,Cambridge, Mass., 1992.
[13] P. Bernard,An Introduction to Default Logic. Springer Verlag, ch. 3, pp. 1330, 1989.
[14] G. Brewka, Nonmonotonic Reasoning: Logical Foundations of Commonsense, Cambridge Trends in Computer Science 12. Cambridge Univ. Press, 1991.
[15] P. Bosc and H. Prade, "An Introduction to Fuzzy Set and Possibility Theory Based Approaches to the Treatment of Uncertainty and Imprecision in Database Management Systems," Proc. Second Workshop Uncertainty Management in Information Systems: From Needs to Solutions,Catalina, Calif., 1993.
[16] P.P. Bonnissone and R.M. Tong, "Editorial: Reasoning with Uncertainty in Expert Systems," Int'l J. Man Machine Studies, vol. 22, pp. 241250, 1985.
[17] B.P. Buckles and F.E. Petry, "Generalised Database and Information Systems," Analysis of Fuzzy Information, vol. 2, J.C. Bezdek, ed., pp. 177201. CRC Press, 1987.
[18] B.P. Buckles and F.E. Petry, "Extension of the Fuzzy Database with Fuzzy Arithmetic," Proc. IFAC Symp., pp. 421426, Marseilles, 1983.
[19] B.P. Buckles and F.E. Petry, "A Fuzzy Representation of Data for Relational Databases," Fuzzy Sets and Systems, vol. 5, pp. 213226, 1982.
[20] R. Cavallo and M. Pittarelli,“The theory of probabilistic databases,” Proc. VLDB Conf. , pp. 7181, 1987.
[21] K.L. Clark, "Negation as Failure," Logic and Databases, H. Gallaire and J. Minker, eds.. New York: Plenum Press, 1978.
[22] M.R.B. Clarke, C. Froidevaux, E. Grégoire, and P. Smets, J. Applied NonClassical Logics, Special Issue on Uncertainty, Conditionals and Nonmonotonicity, 1991.
[23] E. Codd,“Extending the database relational model to capture more meaning,” ACM Trans. Database Systems, vol. 4, no. 4, pp. 397434, 1979.
[24] P.R. Cohen, Heuristic Reasoning about Uncertainty: An Artificial Intelligence Approach.London: Pitman, 1985.
[25] G.F. Cooper,"The computational complexity of probabilistic inference using Bayesian belief networks," Artificial Intelligence, vol. 42, pp. 393405, 1990.
[26] G.F. Cooper and E. Herskovitz,"A Bayesian method for constructing Bayesian belief networks from databases," Proc. 7th Ann. Conf. Uncertainty in Artificial Intelligence, pp. 8694, 1991.
[27] G.F. Cooper and E. Herskovits, "A Bayesian Method for the Induction of Probabilistic Networks from Data," Report SMI193, Section of Medical Informatics, Univ. of Pittsburgh, 1991.
[28] P. Dagum and A. Galper, "Forecasting Sleep Apnea with Dynamic Network Models," Proc. Ninth Conf. Uncertainty in Artificial Intelligence, pp. 6471Washington, 1993.
[29] P. Dagum, A. Galper, and E. Horvitz, "Dynamic Network Models for Forecasting," Proc. Eighth Conf. Uncertainty in Artificial Intelligence, pp. 4148,Stanford, 1992.
[30] R.M. Dawes, "The Robust Beauty of Improper Linear Models in Decision Making," American Psychologist, vol. 34, pp. 571582, 1979.
[31] R. Demolombe and L. Fariñas del Cerro, "An Algebraic Evaluation Method for Deduction in Incomplete Databases," J. of Logic Programming, vol. 5, pp. 183205, 1988.
[32] D. Dubois, J. Lang, and H. Prade, "Towards Possibilistic Logic Programming," Proc. Eighth Int'l Conf. Logic Programming, pp. 581595,Paris, 1991.
[33] D. Dubois, J. Lang, and H. Prade, "Handling Uncertain Knowledge in an ATMS Using Possibilistic Logic," Proc. TMS Workshop European Conf. Artificial Intelligence, pp. 87106,Stockholm, 1990.
[34] D. Dubois, J. Lang, and H. Prade, "Poslog: An Inference System Based upon Possibilistic Logic," Proc. North Am. Fuzzy Information Processing Soc. Congress, pp. 177180,Toronto, 1990.
[35] D. Dubois, J. Lang, and H. Prade, "Automated Reasoning Using Possibilistic Logic: Semantics, Belief Revision and Variable Certainty Weights," Proc. Fifth Workshop on Uncertainty in Artificial Intelligence, pp. 8187, Windsor, 1989.
[36] D. Dubois, J. Lang, and H. Prade, "Theorem Proving Under Uncertainty—A Possibility Theory Based Approach," Proc. 10th Int'l Joint Conf. Artificial Intelligence, pp. 984986, Milan, 1987.
[37] D. Dubois and H. Prade, "Certainty and Uncertainty of (Vague) Knowledge and Generalised Dependencies in Fuzzy Databases," Proc. Int'l Fuzzy Eng. Symp. '91, pp. 239249,Yokahoma, Japan, 1991.
[38] D. Dubois and H. Prade, "Inference in Possibilistic Hypergraphs," Uncertainty in Knowledge Bases, B. BouchonMeunier, R.R. Yager, and L.A. Zadeh, eds., pp. 250259.Berlin: SpringerVerlag, 1990.
[39] D. Dubois and H. Prade, "Resolution Principles in Possibilistic Logic," Int'l J. Approximate Reasoning, vol. 4, pp. 121, 1990.
[40] D. Dubois and H. Prade, Possibility Theory: An Approach to the Computerized Processing of Uncertainty.New York: Plenum Press, 1988.
[41] D. Dubois and H. Prade, "Necessity Measures and the Resolution Principle," IEEE Trans. Systems, Man, and Cybernetics, vol. 17, pp. 474478, 1987.
[42] D. Dubois, H. Prade, and P. Smets, "Partial Truth Is Not Uncertainty: Fuzzy Logic vs. Possibilistic Logic," IEEE Expert, pp. 1519, Aug. 1994.
[43] D. Dubois, H. Prade, and C. Testemale, "Weighted Fuzzy Pattern Matching," Fuzzy Sets and Systems, vol. 28, pp. 313331, 1988.
[44] P.M. Dung, "On the Acceptability of Arguments and Its Fundamental Role in Nonmonotonic Reasoning and Logic Programming," Proc. 13th Int'l Joint Conf. Artificial Intelligence, pp. 852857,Chambéry, France, 1993.
[45] C. Elkan, "The Paradoxical Success of Fuzzy Logic," Proc. 11th Nat'l Conf. Artificial Intelligence, pp. 698703,Washington, D.C., 1993.
[46] M.H. van Emden, “Quantitative Deduction and Its Fixpoint Theory,” J. Logic Programming, vol. 4, no. 1, pp. 3753, 1986.
[47] D. Etherington, Reasoning with Incomplete Information.London: Pitman, 1988.
[48] B. de Finetti, Theory of Probability.New York, Wiley, 1974.
[49] J. Fox, "Decision Theory and Autonomous Systems," Decision Support Systems and Qualitative Reasoning, M.G. Singh and L. TravéMassuyés, eds., NorthHolland, 1991.
[50] J. Fox, "Three Arguments for Extending the Framework of Probability," Uncertainty in Artificial Intelligence, L.N. Kanal, J.F. Lemmer, eds., pp. 447458.Amsterdam: NorthHolland 1986.
[51] J. Fox, "Towards a Reconciliation of Fuzzy Logic and Standard Logic," Int'l J. ManMachine Studies, vol. 15, pp. 213220, 1981.
[52] C. Froidevaux and D. Kayser, "Inheritance in Semantic Networks and Default Logic," NonStandard Logics for Automated Reasoning, P. Smets, E.H. Mamdani, D. Dubois, and H. Prade, eds. London: Academic Press, 1988.
[53] D. Gabbay and A. Hunter, "Making Inconsistency Respectable: A Logical Framework for Inconsistent Reasoning," Fundamentals of Artificial Intelligence Research, Lecture Notes in Artificial Intelligence 535, pp. 1932.Berlin: SpringerVerlag, 1991.
[54] R. George, B.P. Buckles, and F.E. Petry, "An ObjectOriented Data Model to Represent Uncertainty in Coupled Artificial IntelligenceDatabase Systems," The Next Generation of Information Systems: From Data to Knowledge, Lecture Notes in Artificial Intelligence 611, pp. 3748.Berlin: SpringerVerlag, 1991.
[55] M.L. Ginsberg, Readings in Nonmonotonic Reasoning.San Mateo, Calif.: Morgan Kaufmann, 1987.
[56] R.P. Goldman and J.S. Breese, "Integrating Model Construction and Evaluation," Proc. Eighth Conf. Uncertainty in Artificial Intelligence, pp. 104111,Stanford, 1992.
[57] R. Goldman and E. Charniak, “Dynamic Construction of Belief Networks,” Proc. Sixth Conf. Uncertainty in Artificial Intelligence, 1990.
[58] G. Guardalben and D. Lucarella, "Information Retrieval Based on Fuzzy Reasoning," Data and Knowledge Engineering, vol. 10, pp. 2944, 1993.
[59] E. Gunter and L. Libkin, "ORSML: A Functional Database Programming Language for Disjunctive Information and Its Applications," Proc. Fifth Int'l Conf. Database and Expert System Applications, pp. 641650,Athens, 1994.
[60] U. Güntzer, W. Kießling, and H. Thöne, "New Directions for Uncertainty Reasoning in Deductive Databases," Proc. ACM SIGMOD Int'l Conf. Management of Data, pp. 178187,Denver, 1991.
[61] S. Haack, "Do We Need Fuzzy Logic?," Int'l J. ManMachine Studies, vol. 11, pp. 437445, 1979.
[62] J.Y. Halpern, "An Analysis of the FirstOrder Logics of Probability," Proc. 11th Int'l Joint Conf. Artificial Intelligence, pp. 1,3751,381Detroit, 1989.
[63] D.E. Heckerman, E.J. Horvitz, and B.N. Nathwani, "Towards Normative Expert Systems: Part I. The Pathfinder Project," Methods of Information in Medicine, vol. 31, pp. 90105, 1992.
[64] J. Heinsohn, "A Hybrid Approach for Modelling Uncertainty in Terminological Logics," Proc. European Conf. Symbolic and Quantitative Approaches to Uncertainty, pp. 198205,Marseilles, 1991.
[65] E. Herskovitz and G.F. Cooper,"Kutato: An entropydriven system for construction of probabilistic expert systems from databases," Uncertainty in Artificial Intelligence 6, P.P. Bonnisone, M. Henrion, L.N. Kanal, and J.F. Lemmer, eds., NorthHolland, Amsterdam, pp. 117125, 1991.
[66] M.C. Horsch and D. Poole, "A Dynamic Approach to Probabilistic Inference Using Bayesian Networks," Proc. Sixth Conf. Uncertainty in Artificial Intelligence, pp. 155161,Cambridge, Mass., 1990.
[67] G.E. Hughes and M.J. Cresswell, An Introduction to Modal Logic.London: Methuen and Co., 1968.
[68] T. Imielinski, "Incomplete Deductive Databases," Annals of Mathematics and Artificial Intelligence, vol. 3, pp. 259294, 1991.
[69] T. Imielinski and W. Lipski Jr.,“Incomplete information in relational databases,” J. ACM, vol. 31, no. 4, pp. 761791, 1984.
[70] E.T. Jaynes, "Where Do We Stand on Maximum Entropy?" The Maximum Entropy Formalism, R.D. Levine and M. Tribus, eds. Cambridge, Mass.: MIT Press, 1979.
[71] W. Kießling, T. Lukasiewicz, G. Köstler, and U. Güntzer, "The TOP Database Model—Taxonomy, ObjectOrientation and Probability," Proc. Workshop Uncertainty in Deductive Systems and Databases,Ithaca, N.Y., 1994.
[72] K. Konolige, "On the Relation Between Autoepistemic Logic and Circumscription," Proc. 11th Int'l Joint Conf. Artificial Intelligence, pp. 1,2131,218,Detroit, 1989.
[73] K. Konolige, "On the Relation Between Default and Autoepistemic Logic," Artificial Intelligence, vol. 35, pp. 343382, 1988.
[74] Y. Kornatzky and S.E. Shimony, "A Probabilistic ObjectOriented Data Model," Data and Knowledge Engineering, vol. 12, pp. 143166, 1994.
[75] P. Krause, S. Ambler, M. ElvangGøransson, and J. Fox, "A Logic of Argumentation for Uncertain Reasoning," Computational Intelligence, vol. 11, pp. 113131, 1993.
[76] S. Kwan, F. Olken, and D. Rotem, "Uncertain, Incomplete and Inconsistent Data in Scientific and Statistical Databases," Proc. Second Workshop Uncertainty Management and Information Systems: From Needs to Solutions,Catalina, Calif., 1993.
[77] K.B. Laskey and P.E. Lehner, "Assumptions, Beliefs and Probabilities," Artificial Intelligence, vol. 42, pp. 6577, 1990.
[78] S.L. Lauritzen and D.J. Spiegelhalter, "Local Computations with Probabilities on Graphical Structures and Their Application to Expert Systems, J. Royal Statistical Soc., vol. B50, no. 2, pp. 157224, 1988.
[79] S.K. Lee, "An Extended Relational Database Model for Uncertain and Imprecise Information," Proc. 18th Conf. Very Large Databases, pp. 211220,Vancouver, B.C., 1992.
[80] K.S. Leung, M.H. Wong, and W. Lam, "A Fuzzy Expert Database System," Data and Knowledge Engineering, vol. 4, pp. 287304, 1989.
[81] Y.E. Lien,“Multivalued dependencies with null values in relational data bases,” Proc. VLDB Conf. , pp. 6166, 1979.
[82] V. Lifschitz, "Pointwise Circumscription: Preliminary Report," Proc. Fifth Nat'l Conf. Artificial Intelligence,Philadelphia, 1986.
[83] F. Lin and Y. Shoham, "Argument Systems: A Uniform Basis for Nonmonotonic Reasoning," Proc. Int'l Conf. Knowledge Representation and Reasoning, pp. 245255,Toronto, 1989.
[84] W. Lipski,“On semantic issue connected with incomplete information systems,” ACM Trans. Database Systems, vol. 4, no. 3, pp. 262296, 1979.
[85] R.P. Loui, "Defeat Among Arguments: A System of Defeasible Inference," Computational Intelligence, vol. 3, pp. 100106, 1987.
[86] Fuzzy Reasoning and Its Applications, E.H. Mamdani and B.R. Gaines, eds. New York: Academic Press, 1981.
[87] T.P. Martin, J.F. Baldwin, and B.W. Pilsworth, "The Implementation of FProlog—A Fuzzy Prolog Interpreter," Fuzzy Sets and Systems, vol. 23, pp. 119129, 1987.
[88] J. McCarthy, "Applications of Circumscription to Formalising Commonsense Knowledge," Artificial Intelligence, vol. 28, pp. 89116, 1986.
[89] J. McCarthy, "Circumscription—A Form of NonMonotonic Reasoning," Artificial Intelligence, vol. 13, pp. 2739, 1980.
[90] D.V. McDermott, "Nonmonotonic Logic II: Nonmonotonic Modal Theories," J. ACM, vol. 29, pp. 3757, 1982.
[91] D.V. McDermott and J. Doyle, "NonMonotonic Logic I," Artificial Intelligence, vol. 13, pp. 4172, 1980.
[92] M. McLeish, "Nilsson's Probabilistic Entailment Extended to DempsterShafer Theory," Uncertainty in Artificial Intelligence 3, L.N. Kanal, T.S. Levitt, and J.F. Lemmer, eds. North Holland: Elsevier Science, 1989.
[93] M. McLeish, "Probabilistic Logic: Some Comments and Possible Use for Nonmonotonic Reasoning," Uncertainty in Artificial Intelligence 2, J.F. Lemmer and L.N. Kanal, eds. North Holland: Elsevier Science, 1988.
[94] R.C. Moore, "Semantical Considerations on Nonmonotonic Logic," Artificial Intelligence, vol. 25, pp. 7594, 1985.
[95] J.M. Morrissey, "Representing and Manipulating Uncertain Data," Int'l J. ManMachine Studies, vol. 36, pp. 183189, 1992.
[96] J.M. Morrissey,“Imprecise information and uncertainty in information systems,” ACM Trans. Information Systems, vol. 8, no. 2, pp. 159180, 1990.
[97] A. Motro, "Sources of Uncertainty in Information Systems," Proc. Second Workshop Uncertainty Management and Information Systems: From Needs to Solutions,Catalina, Calif., 1993.
[98] A. Motro, “Accommodating Imprecision in Database Systems: Issues and Solutions,” Proc. ACM SIGMOD Record, vol. 19, no. 4, pp. 69–74, Dec. 1990.
[99] S.A. Musman and L.W. Chong, "A Study of Scaling Issues in Bayesian Belief Nets for Ship Classification," Proc. Ninth Conf. Uncertainty in Artificial Intelligence,Washington, 1993.
[100] R. Neapolitan, Probabilistic Reasoning in Expert Systems: Theory and Algorithms, John Wiley&Sons, New York, 1990.
[101] R. Ng and V.S. Subrahmanian, "Probabilistic Logic Programming," Information and Computation, vol. 101, no. 2, pp. 150201, 1992.
[102] R.T. Ng and V.S. Subrahmanian, "Empirical Probabilities in Monadic Deductive Databases," Proc. Eighth Conf. Uncertainty in Artificial Intelligence, pp. 215222,Stanford, 1992.
[103] R.T. Ng and V.S. Subrahmanian, "NonMonotonic Negation in Probabilistic Deductive Databases," Proc. Seventh Conf. Uncertainty in Artificial Intelligence, pp. 249256,Los Angeles, 1991.
[104] R.T. Ng and V.S. Subrahmanian, "A Semantical Framework for Supporting Subjective and Conditional Probabilities in Deductive Databases," Proc. Eighth Int'l Conf. Logic Programming, pp. 566580,Paris, 1991.
[105] J.Y. Nie, "Towards a Probabilistic Modal Logic for SemanticBased Information Retrieval," Proc. 15th Ann. ACM SIGIR Conf. Research and Development in Information Retrieval, N. Belkin, P. Ingerwersen, and A. Pejtersen, eds., pp. 140151, 1992.
[106] N. Nilsson, "Probabilistic Logic Revisited," Artificial Intelligence, vol. 59, pp. 3942, 1993.
[107] N. Nilsson, "Probabilistic Logic," Artificial Intelligence, vol. 28, pp. 7187, 1986.
[108] K.G. Olesen, U. Kjaerulff, F. Jensen, F.V. Jensen, B. Falck, S. Andraessen, and S.K. Andersen, "A MUNIN Network for the Median Nerve—A Case Study in Loops," Applied Artificial Intelligence, vol. 3, pp. 385404, 1989.
[109] S. Parsons, Qualitative Approaches to Reasoning Under Uncertainty. MIT Press, 1996.
[110] S. Parsons, "Hybrid Models of Uncertainty in Protein Topology Prediction," Applied Artificial Intelligence, vol. 9, pp. 335351, 1994.
[111] S. Parsons, M. Kubat, and M. Dohnal, "A Rough Set Approach to Reasoning Under Uncertainty," J. Experimental and Theoretical AI, vol. 7, pp. 175193, 1995.
[112] Z. Pawlak, Rough Sets—Theoretical Aspects of Reasoning about Data. Kluwer Academic, 1991.
[113] J. Pearl, Probabilistic Reasoning in Intelligent Systems. San Mateo, Calif.: Morgan Kaufman, 1988.
[114] M. Pittarelli, "An Algebra for Probabilistic Databases," IEEE Trans. Knowledge and Data Engineering, vol. 6, pp. 293303, 1994.
[115] M. Pittarelli, "Probabilistic Databases for Decision Analysis," Int'l J. Intelligent Systems, vol. 5, pp. 209236, 1990.
[116] J. Pollack, "How to Reason Defeasibly," Artificial Intelligence, vol. 57, pp. 142, 1992.
[117] D. Poole, “Probabilistic Horn Abduction,” Artificial Intelligence, vol. 64, no. 1, pp. 81129, 1993.
[118] D. Poole, "Representing Bayesian Networks Within Probabilistic Horn Clause Abduction," Proc. Seventh Conf. Uncertainty in Artificial Intelligence, pp. 271278,Los Angeles, 1991.
[119] D. Poole, "Explanation and Deduction: An Architecture for Default and Abductive Reasoning," Computational Intelligence, vol. 5, pp. 97110, 1989.
[120] D.L. Poole, "A Logical Framework for Default Reasoning," Artificial Intelligence, vol. 36, pp. 2747, 1988.
[121] D.L. Poole, R.G. Goebel, and R. Aleliunas, "Theorist: A Logical Reasoning System for Defaults and Diagnosis," The Knowledge Frontier: Essays in the Representation of Knowledge, N. Cercone and G. McCalla, eds. New York: Springer Verlag, 1987.
[122] H. Prade, "Lipski's Approach to Incomplete Information Databases Restated and Generalized in the Setting of Zadeh's Possibility Theory," Information Systems, vol. 9, pp. 2742, 1984.
[123] H. Prade and C. Testemale, "Generalizing Database Relational Algebra for the Treatment of Incomplete or Uncertain Information and Vague Queries," Information Sciences, vol. 34, pp. 115143, 1984.
[124] H. Prade and C. Testemale, "Fuzzy Relational Databases: Representational Issues and Reduction Using Similarity Measures," J. Am. Soc. Information Science, vol. 38, pp. 118126, 1987.
[125] H. Prade and C. Testemale, "Representation of Soft Constraints and Fuzzy Attribute Values by Means of Possibility Distributions in Databases," Analysis of Fuzzy Information, vol. 2, J.C. Bezdek, ed., pp. 213229. CRC Press, 1987.
[126] H. Prade and C. Testemale, "Application of Possibility and Necessity Measures to Documentary Information Retrieval," Uncertainty in KnowledgeBased Systems, B. Bouchon and R.R. Yager, eds., Lecture Notes in Computer Science 286, pp. 265274.Berlin: SpringerVerlag, 1987.
[127] G. Provan, "Dynamic Network Updating Techniques for Diagnostic Reasoning," Proc. Seventh Conf. Uncertainty in Artificial Intelligence, pp. 279286,Los Angeles, 1991.
[128] G. Provan, "Solving Diagnostic Problems Using Extended AssumptionBased Truth Maintenance Systems: Foundations," Technical Report 8810, Dept. of Computer Science, Univ. of British Columbia, 1988.
[129] J.R. Quinlan, "INFERNO: A Cautious Approach to Uncertain Inference," The Computer J., vol. 26, pp. 255269, 1983.
[130] R. Reiter, "A Logic for Default Reasoning," Artificial Intelligence, vol. 13, pp. 81132, 1980.
[131] R. Reiter, "On Closed World Databases," Logic and Databases, H. Gallaire and J. Minker, eds. New York: Plenum Press, 1978.
[132] C.J. Van Rijsbergen, "A NonClassical Logic for Information Retrieval," The Computer J., vol. 29, pp. 481485, 1986.
[133] A. Saffiotti, "A Belief Function Logic," Proc. 10th Nat'l Conf. Artificial Intelligence, pp. 642647Boston, 1992.
[134] A. Saffiotti, "A Belief Function Logic: Preliminary Report," Technical Report, TR/IRIDIA/9125, IRIDIA, Univ. Libre de Bruxelles, 1992.
[135] A. Saffiotti, "A Hybrid Framework for Representing Uncertain Knowledge," Proc. Eighth Nat'l Conf. Artificial Intelligence, pp. 653658,Boston, 1990.
[136] A. Saffiotti, "An AI View of the Treatment of Uncertainty," The Knowledge Engineering Review, vol. 2, pp. 7597, 1987.
[137] A. Saffiotti, S. Parsons, and E. Umkehrer, "Comparing Uncertainty Management Techniques," Microcomputers in Civil Eng.—Special Issue on Uncertainty in Expert Systems, vol. 9, pp. 367380, 1994.
[138] A. Saffiotti and E. Umkehrer, "Pulcinella: A General Tool for Propagating Uncertainty in Valuation Networks," Proc. Seventh Conf. Uncertainty in Artificial Intelligence, pp. 323331Los Angeles, 1991.
[139] A. Saffiotti and E. Umkehrer, "Automatic Generation of Valuation Networks from General Clauses," Technical Report,TR/IRIDIA/9124, IRIDIA, Univ. Libre de Bruxelles, 1991.
[140] S. Schocken and R.A. Hummel, "On the Use of the Dempster Shafer Model in Information Indexing and Retrieval Applications," Int'l J. ManMachine Studies, vol. 39, pp. 843879, 1993.
[141] G. Shafer, A Mathematical Theory of Evidence. Princeton Univ. Press, 1976.
[142] S. Shenoi and A. Melton, "Proximity Relations in the Fuzzy Relational Database Model," Fuzzy Sets and Systems, vol. 31 pp. 285296, 1989.
[143] P.P. Shenoy, "Using Possibility Theory in Expert Systems," Fuzzy Sets and Systems, vol. 51, pp. 129142, 1992.
[144] P.P. Shenoy and G. Shafer, "Axioms for Probability and Belief Function Propagation," Uncertainty in Artificial Intelligence 4, R.D. Shacter, T.S. Levitt, L.N. Kanal, and J.F. Lemmer, eds. North Holland: Elsevier Science, 1990.
[145] P. Smets, "Belief Functions," NonStandard Logics for Automated Reasoning, P. Smets, E.H. Mamdani, D. Dubois, and H. Prade, eds., London: Academic Press, 1988.
[146] P. Smets and P. Magrez, "Implication in Fuzzy Logic," Int'l J. Approximate Reasoning, vol. 1, pp. 327347, 1987.
[147] C.A.B. Smith, "Consistency in Statistical Inference and Decision," J. Royal Statistical Soc., vol. B23, pp. 218258, 1961.
[148] M. Smithson, Ignorance and Uncertainty: Emerging Paradigms.New York: Springer Verlag, 1989.
[149] S. Srinivas and J. Breese, "IDEAL: A Software Package for Analysis of Influence Diagrams," Proc. Sixth Conf. Uncertainty in Artificial Intelligence, pp. 212219,Cambridge, Mass., 1990.
[150] S. Srinivas,S. Russell,, and A. Agogino,"Automated construction of sparse Bayesian networks from unstructured probabilistic models and domain information," Uncertainty in Artificial Intelligence 5, NorthHolland, pp. 295308, 1990.
[151] M. Studeny, "Conditional Independence Relations Have No Finite Complete Characterisation," Information Theory, Statistical Decision Functions, Random Processes: Trans. 11th Prague Conf., S. Kubík and J.A. Vísek, eds. Dordrecht: Kluwer, 1992.
[152] L.E. Sucar, D.F. Gillies, and D.A. Gillies, "Objective Probabilities in Expert Systems," Artificial Intelligence, vol. 61, pp. 187208, 1993.
[153] H. Thöne, U. Güntzer, and W. Kießling, "Towards Precision of Probabilistic Bounds Propagation," Proc. Eighth Conf. Uncertainty in Artificial Intelligence, pp. 315322,Stanford, 1992.
[154] H.R. Turtle and W.B. Croft, "Uncertainty in Information Retrieval Systems," Proc. Second Workshop Uncertainty Management and Information Systems: From Needs to Solutions,Catalina, Calif., 1993.
[155] H.R. Turtle and W.B. Croft, "Inference Networks for Document Retrieval," Proc. 13th Int'l Conf. Research and Development in Information Retrieval, pp. 124, ACM, 1990.
[156] M. Umano, "Retrieval from Fuzzy Database by Fuzzy Relational Algebra," Proc. IFAC Symp., pp. 16, Marseilles, 1983.
[157] M.P. Wellman, J.S. Breese, and R.P. Goldman, "From Knowledge Bases to Decision Models," The Knowledge Eng. Review, vol. 7, pp. 3553, 1992.
[158] K.G. Waugh, M.H. Williams, Q. Kong, S. Salvani, and G. Chen, "Designing SQUIRREL: An Extended SQL for a Deductive Database System," The Computer J., vol. 33, no. 6, pp. 535546, 1990.
[159] W.X. Wen, "From Relational Databases to Belief Networks," Proc. Seventh Conf. Uncertainty in Artificial Intelligence, pp. 406413,Los Angeles, 1991.
[160] M.H. Williams and Q. Kong, "Time and Incompleteness in a Deductive Database," Uncertainty in Knowledge Bases, B. BouchonMeunier, R.R. Yager, and L.A. Zadeh, eds., pp. 443455.Berlin: SpringerVerlag, 1991.
[161] M.H. Williams and Q. Kong, "Incomplete Information in a Deductive Database," Data and Knowledge Eng., vol. 3, pp. 197220, 1988.
[162] E. Wong, "A Statistical Approach to Incomplete Information in Database Systems," ACM Trans. Database Systems, vol. 7, no. 3, pp. 470488, Sept. 1982.
[163] S.K.M. Wong, Y. Xiang, and X. Nie, "Representation of Bayesian Networks as Relational Databases," Proc. Int'l Conf. Information Processing and Management of Uncertainty, pp. 159165,Paris, 1994.
[164] L.A. Zadeh, "Fuzzy Probabilities," Information Processing&Management, vol. 3, pp. 363372, 1984.
[165] L.A. Zadeh, "Commonsense Knowledge Representation Based on Fuzzy Logic," Computer, pp. 6165, Oct. 1983.
[166] L.A. Zadeh, "The Role of Fuzzy Logic in the Management of Uncertainty in Expert Systems," Fuzzy Sets and Systems, vol. 11, pp. 199227, 1983.
[167] L.A. Zadeh, "Fuzzy Sets as the Basis for a Theory of Possibility," Fuzzy Sets and Systems, vol. 1, pp. 127, 1978.
[168] L.A. Zadeh, "Fuzzy Sets," Information and Control, vol. 8, pp. 338353, 1965.
[169] C. Zaniolo, "Database Relations with Null Values," Proc. ACM Symp. Principles of Database Systems, pp. 2733,Los Angeles, 1982.
[170] R. Zicari, "Databases and Incomplete Information," Proc. Second Workshop Uncertainty Management and Information Systems: From Needs to Solutions,Catalina, Calif., 1993.
[171] R. Zicari, "Incomplete Information in ObjectOriented Databases," SIGMOD RECORD, vol. 19, no. 3, pp. 516, 1990.
[172] A. Zvelli, "A Fuzzy Relational Calculus," Proc. First Int'l Conf. Expert Database Systems, pp. 311326,Charleston, S.C., 1986