A new approach for modelling uncertainty in expert systems knowledge bases

• Autorzy: Niederliński A.

Identyfikatory

DOI

10.24425/119075

• Języki publikacji: EN

Abstrakty

EN

The current paradigm of modelling uncertainty in expert systems knowledge bases using Certainty Factors (CF) has been critically evaluated. A way to circumvent the awkwardness, non-intuitiveness and constraints encountered while using CF has been proposed. It is based on introducing Data Marks for askable conditions and Data Marks for conclusions of relational models, followed by choosing the best suited way to propagate those Data Marks into Data Marks of rule conclusions. This is done in a way orthogonal to the inference using Aristotelian Logic. Using Data Marks instead of Certainty Factors removes thus the intellectual discomfort caused by rejecting the notion of truth, falsehood and the Aristotelian law of excluded middle, as is done when using the CF methodology. There is also no need for changing the inference system software (expert system shell): the Data Marks approach can be implemented by simply modifying the knowledge base that should accommodate them. The methodology of using Data Marks to model uncertainty in knowledge bases has been illustrated by an example of SWOT analysis of a small electronic company. A short summary of SWOT analysis has been presented. The basic data used for SWOT analysis of the company are discussed. The rmes_EE SWOT knowledge base consisting of a rule base and model base have been presented and explained. The results of forward chaining for this knowledge base have been presented and critically evaluated.

Słowa kluczowe

EN

expert systems; uncertainty; certainty factors; knowledge bases; data marks; SWOT; SWOT knowledge base

• Wydawca: Polish Academy of Sciences, Committee of Automatic Control and Robotics
• Czasopismo: Archives of Control Sciences
• Rocznik: 2018
• Tom: Vol. 28, No. 1
• Strony: 19--34
• Opis fizyczny: Bibliogr. 12 poz., rys., tab., wzory

Twórcy

autor

Niederliński A.

• University of Economics in Katowice, Poland

Bibliografia

• [1] B. Buchanan and E. Shortliffe : Rule-Based Expert Systems: The MYCIN Experiments. Addison-Wesley, Reading, MA, 1984.
• [2] D. Heckerman : The Certainty-Factor Model, [e-mail], 2017
• [3] D. W. Hubbard : How to Measure Anything. Finding the Value of ‘Intangibles’ in Business. John Wiley and Sons, Inc. 2010.
• [4] A. S. Humphrey : SWOT Analysis for Management Consulting. https://www.sri.com/sites/default/files/brochures/dec-05.pdf, 2017.
• [5] A. Niederliński : rmes – Rule and Model-Based Expert Systems. PKJS, Gliwice, second edition, 2011.
• [6] A. Niederliński : rmes – Rule and Model-Based Expert Systems. PKJS, Gliwice, second edition, 2011.
• [7] A. Niederliński : Rule- and Model-Based expert systems, http://www.rmes.pl, 2017.
• [8] A. Niederliński : Data Marks for Uncertainty Management in Expert System Knowledge Bases. Proceedings of the 25 ISD Conference, Katowice, 2016, Ed. M. Pańkowska, 531–535.
• [9] C. Nikolopoulos : Expert systems. Marcel De4kker, Inc., N. Your, 1997.
• [10] E. Shortliffe : Computer Based Medical Consultations: MYCIN. American Elsevier, New York, 1976.
• [11] W. Turban, R. Sharda and D. Delen : Decision Support and Business Intelligence System. Pearson, Boston, 2011.
• [12] Wikipedia. SWOT Analysis, https://en.wikipedia.org/wiki/SWOT analysis, 2017.

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).