Dealing with Uncertainty in Expert Systems
Sonal Dubey1, R. K. Pandey2, S. S. Gautam3
1Smt. Sonal Dubey, Research Scholar, Faculty of Science & Environment, Mahatma Gandhi Gramodaya Vishwa Vidyalaya Chitrakoot, Satna, (M.P.), India.
2R. K. Pandey, Reader, University Institute of Computer Science and Applications, Rani Durgavati Vishwa Vidyalaya, Jabalpur (MP.), India.
3Dr. S. S. Gautam, Reader, Faculty of Science & Environment Mahatma Gandhi Gramodaya Vishwa Vidyalaya Chitrakoot Satna (M.P.), India.
Manuscript received on June 25, 2014. | Revised Manuscript received on July 03, 2014. | Manuscript published on July 05, 2014. | PP: 105-111 | Volume-4, Issue-3, July 2014. | Retrieval Number: C2315074314/2012©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The aim of artificial intelligence is to develop tools for representing piece of knowledge and providing inference mechanism for elaborating conclusion of knowledge from stored information. The available knowledge is far from being certain, precise and complete. In Expert systems the word uncertainty is related to the working with inexact data, imprecise information, handling identical situation, reliability of the results etc. An expert system allows the user to assign probabilities, certainty factors, or confidence levels and many more techniques to any or all input data. This feature closely represents how most problems are handled in the real world. An expert system can take all relevant factors into account and make a recommendation based on the best possible solution rather than the only exact solution to handle such problems. This paper describes the various types of uncertainty, its sources and different approaches to handle uncertainty.
Keywords: Certainty factor, expert system, fuzzy logic, soft computing uncertainty management.