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Uncertanity Handling in Knowledge Based Systems


Sandhia Valsala, Bindhya Thomas


Vol. 11  No. 10  pp. 120-126


The traditional goal in the field Artificial Intelligence (AI) is to develop computer based system that can exhibit intelligence. The true applications of AI are precisely the Knowledge Based Systems (KBS). These systems possess the knowledge at an expert level in a specific domain such as medicine, law, engineering, etc. One of the most important intelligent activity of human beings is decision making. The term uncertainty refers to “ imprecise or insufficient knowledge”. The most challenging part is making decisions based on this uncertain data. This brings out a special domain namely, uncertainty handling in KBS in the field of AI . In this paper we consider various methods of handling Uncertanity in Knowledge Based systems .The paper also presents a comparative study of Evidence Point mechanisms with Bayesian Theorem ,Dempster Shafer model and Fuzzy Logic.


KBS,Uncertanity, Evidence point mechanisams