Which methods are used for handling uncertainty in rule based expert system?
The management of uncertainty in expert systems has usually been left to ad hoc representations and combining rules lacking either a sound theory or clear semantics. However, the aggregation of uncertain information (facts) is a recurrent need in the reasoning process of an expert system. Facts must be aggregated to determine the degree to which the premise of a given rule has been satisfied, to verify the extent to which external constraints have been met, to propagate the amount of uncertainty through the triggering of a given rule, to summarize the findings provided by various rules or knowledge sources or experts, to detect possible inconsistencies among the various sources, and to rank different alternatives or different goals. Show
There is no uniformly accepted approach to solve this issue. A variety of approaches will be described as part of the review of the state of the art in reasoning with uncertainty. We will examine quantitative and qualitative characterizations of uncertainty. Among the quantitative approaches, we will cover single-valued approaches (Bayes Rule, Modified Bayes Rule, Confirmation Theory); interval-valued approaches (Dempster-Shafer Theory, Probability Bounds, Evidential Reasoning); and fuzzy-valued approaches (Necessity and Possibility Theory). Among the qualitative approaches we will exmine formal (Reasoned Assumptions) and heuristic (Endorsements). These approaches will then be evaluated according to a list of criteria that reflect a crucial set of requirements common to a large variety of problems. Keywords
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Learn about institutional subscriptions PreviewUnable to display preview. Download preview PDF. Unable to display preview. Download preview PDF. Picro P. Bonissone. Plausible Reasoning: Coping with Uncertainty in Expert Systems. In Stuart Shapiro, Editor, Encyclopedia of Artificial Intelligence, pages 854– 863. John Wiley and Sons Co., New York, 1987. Google Scholar Picro P. Bonissone. Summarizing and Propagating Uncertain Information with Triangular Norms. International Journal of Approximate Reasoning, 1(1 ):71–101, January 1987. CrossRef MathSciNet Google Scholar Piero P. Bonissone and Richard M. Tong. Editorial: Reasoning with Uncertainty in Expert Systems. International Journal of Man-Machine Studies, 22(3):241–250, 1985. Google Scholar L.A. Zadeh. Syllogistic reasoning in fuzzy logic and its application to usuality and reasoning with dispositions. IEEE Trans. Systems, Man and Cybernetics, SMC-15:754–765, 1985. MathSciNet Google Scholar L.A. Zadeh. Dispositional logic. Appl. Math. Letters, l(l):95–99, 1988. CrossRef MATH MathSciNet Google Scholar L.A. Zadeh. A computational approach to fuzzy quantifiers in natural language. Computers and Mathematics, 9:149–184, 1983. MATH MathSciNet Google Scholar L.A. Zadeh. A computational theory of disposition. In Proc. 1984 Int. Conf. Computational Linguistics, pages 312–318, 1984. CrossRef Google Scholar B. Schweizer and A. Sklar. Associative Functions and Abstract Semi-Groups. Publicationcs Mathematicae Debrecen, 10:69–81, 1963. MathSciNet Google Scholar D. Dubois and H. Prade. Criteria Aggregation and Ranking of Alternatives in the Framework of Fuzzy Set Theory. In H.J. Zimmerman, L.A. Zadeh, and B.R. Gaines, Editors, TIMS/Studies in the Management Science, Vol. 20, pages 209–240. Elsevier Science Publishers, 1984. Google Scholar P. Bonissone and K.Decker. Selecting uncertainty calculi and granularity: An experiment in trading-off precision and complexity. In L. N. Kanaal and J.F. Lemmer, Editors, Uncertainty in Artificial Intelligence. North Holland, Amsterdam, 1986. Google Scholar L.A. Zadch. Fuzzy logic and approximate reasoning (in memory of Grigor Moisil). Synlhese, 30:407–428, 1975. CrossRef MATH Google Scholar J. Doyle. Methodological SimpliCity in Expert System Construction: The Case of Judgements and Reasoned Assumptions. The AI Magazine, 4(2):39–43, 1983. MathSciNet Google Scholar L.A. Zadch. Review of Books: A Mathematical Theory of Evidence. The AI Magazine, 5(3):8l–83, 1984 Google Scholar L.A. Zadeh. A simple view of the dempster-shafer theory of evidence and its implications for the rule of combinations. Technical Report 33, Berkeley Cognitive Science, Institute of Cognitive Science, University of California, Berkeley,, 1985. Google Scholar A.P. Dempster. Upper and lower probabilities induced by a multivalued mapping. Annals of Mathematical Statistics, 38:325–339, 1967. CrossRef MATH MathSciNet Google Scholar J. Pearl. Reverend Bayes on Inference Engines: a Distributed Hierarchical Approach. In Proceedings Second National Conference on Artificial Intelligence, pages 133–136. AAAI, August 1982. Google Scholar J. Pearl. How to Do with Probabilities What People Say You Can’t. In Proceedings Second Conference on Artificial Intelligence Applications, pages 1–12. IEEE, December 1985. Google Scholar Judea Pearl. Evidential Reasoning Under Uncertainty. In Howard E. Shrobe, Editor, Exploring Artificial Intelligence, pages 381–418. Morgan Kaufmann, San Mateo, CA, 1988. Google Scholar R.O. Duda, P.E. Hart, and N.J. Nilsson. Subjective Bayesian methods for rule-based inference systems. In Proc. AFIPS 45, pages 1075–1082, New York, 1976. AE1PS Press. Google Scholar E.H. Shortliffe and B. Buchanan. A model of inexact reasoning in medicine. Mathematical Bioscicnces, 23:351–379, 1975. CrossRef MathSciNet Google Scholar A.P. Dempster. A generalization of Bayesian inference. J. Roy. Stat. Soc, Ser. B, 30:205–247, 1968. MathSciNet Google Scholar G. Shafer. A Mathematical Theory of Evidence. Princeton University Press, Princeton, New Jersey, 1976. MATH Google Scholar T.D. Garvey, J.D. Lowrance, and M.A. Fischler. An inference technique for integrating knowledge from disparate sources. In Proc. 7lh. Intern. Joint Conf. on Artificial Intelligence, Vancouver, British Columbia, Canada, 1981. Google Scholar J. Lowrance and T.D. Garvey. Evidential reasoning: an implementation for multisensor integration. Technical Report Technical Note 307, SRI International, Artificial Intelligence Center, Menlo Park, California, 1983. Google Scholar J.D. Lowrance, T.D. Garvey, and T.M. Strrat. A framework for evidential-reasoning systems. In Proc. National Conference on Artificial Intelligence, pages 896–903, Menlo Park, California, 1986. AAAI. Google Scholar J.R. Quinlan. Consistency and Plausible Reasoning. In Proceedings Eight International Joint Conference on Artificial Intelligence, pages 137–144. AAAI, August 1983. Google Scholar C.R. Rollinger. How to Represent Evidence — Aspects of Uncertainty Reasoning. In Proceedings Eight International Joint Conference on Artificial Intelligence, pages 358–361. AAAI, August 1983. Google Scholar L.A. Zaclch. Fuzzy sets as a. basis for a theory of possibility. Fuzzy Scts and Systems, 1:3–28, 1978. CrossRef Google Scholar L.A. Zadeh. Fuzzy Sets and Information Granularity. In M.M. Gupta, R.K. Ragade, and R.R. Yager, Editors, Advances in Fuzzy Set Theory and Applications, pages 3–18. Elsevier Science Publishers, 1979. Google Scholar L.A. Zadeh. A theory of approximate reasoning. In P. Hayes, D. Michie, and L.I. Mikulich, Editors, Machine Intelligence, pages 149–194. Halstead Press, New York, 1979. Google Scholar L.A. Zadeh. Linguistic variables, approximate reasoning, and dispositions. Medical Informatics, 8:173–186, 1983. CrossRef Google Scholar R. Reiter. A Logic for Default Reasoning. Journal of Artificial Intelligence, 13:81– 132, 1980. CrossRef MATH MathSciNet Google Scholar P. Cohen. Heuristic Reasoning about Uncertainty: An Artificial Intelligence Approach. Pittman, Boston, Massachusetts, 1985. Google Scholar P.R. Cohen and M.R. Grinberg. A Theory of Heuristics Reasoning about Uncertainty. The Al Magazine, pages 17–233, 1983. Google Scholar P.R. Cohen and M.R. Grinberg. A Framework for Heuristics Reasoning about Uncertainty. In Proceedings Eight International Joint Conference on Artificial Intelligence, pages 355–357. AAAI, August 1983. Google Scholar E.D. Pednault, S.W. Zucker, and L.V. Muresan. On the Independence Assumption Underlying Subjective Bayesian Updating. Journal of Artificial Intelligence, 16:213– 222, 1981. CrossRef MATH MathSciNet Google Scholar C. Glymour. Independence Assumptions and Bayesian Updating. Journal of Artificial Intelligence, 25:95–99, 1985. CrossRef MATH MathSciNet Google Scholar R.W. Johnson. Independence and Bayesian Updating Methods. Journal of Artificial Intelligence, 29:217–222, 1986. CrossRef MATH Google Scholar Yizong Cheng and Rangasami Kashyap. Irrelevancy of evidence caused by independence assumptions. Technical Report TR-EE 86–17, School of Electrical Engineering, Purdue University, West Lafayette, Indiana 47907, 1986. Google Scholar R. Giles. Semantics for Fuzzy Reasoning. International Journal of Man-Machine Studies, 17(4):401–415, 1982. CrossRef MATH Google Scholar M. Ishizuka, K.S. Fu, aud J.T.P. Yao. Inexact Inference for Rule-Based Damage Assessment of Existing Structure. In Proceedings Seventh International Joint Conference on Artificial Intelligence, pages 1837–842. AAAI, August 1981. Google Scholar M. Ishizuka. An extension of dempster-shafer theory to fuzzy sets for constructing expert systems. Seisan-Kenkyu, 34:312–315, 1982. Google Scholar B.C. Buchanan and E.H. Shortlifle. Rule-Based Expert Systems. Addison-Wesley, Reading, Massachusetts, 1984. Google Scholar D. Heckerman. Probabilistic interpretations for MYCIN certainty factors. In L.N. Kanaal and J.F. Lemmer, Editors, Uncertainty in Artificial Intelligence, pages 167– 196. North-Holland, Amsterdam, 1986. Google Scholar E. Rich. Default Reasoning as Likelihood Reasoning. In Proceedings Third National Conference on Artificial Intelligence, pages 348–351. AAAI, August 1983. Google Scholar J.A. Barnett. Computational methods for a mathematical theory of evidence. In Proc. 7th. Intern. Joint Conf on Artificial Intelligence, Vancouver, British Columbia, Canada,, 1981. Google Scholar T.M. Strat. Continuous belief functions for evidential reasoning. In Proc. National Conference on Artificial Intelligence, pages 308–313, Austin, Texas,, 1984. Google Scholar D. Dubois and H. Prade. Combination and Propagation of Uncertainty with Belief Functions — A Reexamination. In Proceedings Ninth International Joint Conference on Artificial Intelligence, pa,ges 111–113. AAAI, August 1985. Google Scholar M.L. Ginsberg. Non-Monotonic Reasoning Using Dempster’s Rule. In Proceedings Fourth National Conference on Artificial Intelligence, pages 126–129. AAAI, August 1984. Google Scholar P. Smets. The Degree of Belief in a Fuzzy Set. Information Science, 25:1–19, 1981. CrossRef MATH MathSciNet Google Scholar P. Smcts. Belief functions. In P. Smets, A. Mamdani, D. Dubois, and H. Prade, Editors, Non-Standard Logics for Automated Reasoning. Academic Press, New York, 1988. Google Scholar L.A. Zadeh. Fuzzy sets. Information and Control, 8:338–353, 1965. CrossRef MATH MathSciNet Google Scholar H. Prade. A Computational Approach to Approximate Reasoning and Plausible Reasoning with Applications to Expert Systems. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-7(3):260–283, 1985. CrossRef Google Scholar Piero P. Bonissone and Allen L. Brown. Expanding the Horizons of Expert Systems. In T. Bernold, Editor, Expert Systems and Knowledge Engineering, pages 267–288. North-Holland, 1986. Google Scholar Piero P. Bonissone. Using T-norm Based Uncertainty Calculi in a Naval Situation Assessment Application. In Proceedings of the Third AAAI Workshop on Uncertainty in Artificial Intelligence, pages 250–261. AAAI, July 1987. Google Scholar Piero P. Bonissone and Nancy C Wood. Plausible Reasoning in Dynamic Classification Problems. In Proceedings of the Validation and Testing of Knowledge-Based Systems Workshop. AAAI, August 1988. Google Scholar Piero P. Bonissone, Stephen Cans, and Keith S. Decker. RUM: A Layered Architecture for Reasoning with Uncertainty. In Proceedings 10th International Joint Conference on Artificial Intelligence, pages 891–898. AAAI, August 1987. Google Scholar Download references Author informationAuthors and Affiliations
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Rights and permissionsReprints and Permissions Copyright information© 1989 Springer-Verlag Berlin, Heidelberg About this paperCite this paperBonissone, P.P. (1989). Uncertainty in Kbs (Expert Systems). In: Jovanović, A.S., Kussmaul, K.F., Lucia, A.C., Bonissone, P.P. (eds) Expert Systems in Structural Safety Assessment. Lecture Notes in Engineering, vol 53. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-83991-7_6 What are the various methods of handling uncertainty?Four methods of uncertainty analysis are explored; interval mathematics, Monte Carlo simulation with triangularly distributed input parameters, Monte Carlo simulation with normally distributed input parameters, and fuzzy set theory.
How uncertainty is managed in an expert system?The management of uncertainty in expert systems has usually been left to ad hoc representations and combining rules lacking either a sound theory or clear semantics. However, the aggregation of uncertain information (facts) is a recurrent need in the reasoning process of an expert system.
What are the 3 techniques in uncertainty reasoning?We will look at one simple way of handling uncertain answers, and three different methods of dealing with uncertain reasoning: r confidence factors r probabilistic reasoning r fuzzy logic.
Which of the following is a ruleA rule-based expert system has five components: the knowledge base, the database, the inference engine, the explanation facilities, and the user interface. 1- The knowledge base contains the domain knowledge useful for problem solving.
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