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.

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

  • Expert System
  • Belief Function
  • Possibility Distribution
  • Possibility Theory
  • Approximate Reasoning

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Authors and Affiliations

  1. General Electric Corporate Research and Development, Artificial Intelligence Program, Schenectady, New York, 12301, USA

    Piero P. Bonissone

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  1. Piero P. Bonissone

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Editors and Affiliations

  1. Staatliche Materialprüfungsanstalt (MPA), Universität Stuttgart, Pfaffenwaldring 32, Stuttgart 80, 7000, Germany

    Aleksandar S. Jovanović & Karl F. Kussmaul & 

  2. Institute for Systems Engineering JRC Ispra, Ispra (VA), I-21020, Italy

    Alfredo C. Lucia

  3. General Electric Company, CRD-K-1, 5C32A, Schenectady, NY, 12301, USA

    Piero P. Bonissone

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Bonissone, 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 rule

A 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.