Methods of dealing with uncertainty in expert system
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Representing uncertainty in expert systems from bhupendra kumar Show
3. Textbook for a course in expert systems,if an emphasis is placed on Chapters 1 to 3 and on a selection of material from Chapters 4 to 7. There is also the option of using an additional commercially available sheU for a programming project. In assigning a programming project, the instructor may use any part of a great variety of books covering many subjects, such as car repair. Instructions for mostofthe "weekend mechanic" books are close stylisticaUy to expert system rules. Contents Chapter 1 gives an introduction to the subject matter; it briefly presents basic concepts, history, and some perspectives ofexpert systems. Then itpresents the architecture of an expert system and explains the stages of building an expert system. The concept of uncertainty in expert systems and the necessity of deal ing with the phenomenon are then presented. The chapter ends with the descrip tion of taxonomy ofexpert systems. Chapter 2 focuses on knowledge representation. Four basic ways to repre sent knowledge in expert systems are presented: first-order logic, production sys tems, semantic nets, and frames. Chapter 3 contains material about knowledge acquisition. Among machine learning techniques, a methodofrule learning from examples is explained in de tail. Then problems ofrule-base verification are discussed. In particular, both consistency and completeness oftherule base are presented. 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. 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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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 How expert systems deal with uncertainty?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.
What are the various methods to deal with the uncertainty in the knowledge system discuss any one of them?Research in the expert system domain has provided a variety of methods for dealing with the uncertainty problem. Among them are probability theory, uncertainty theory, the Dempster ISchafer theory, possibility theory, plausibility theory, etc.
What refers to Minimising the global uncertainties of the entire expert system?Validation refers to minimizing the global uncertainties of the entire expert system.
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