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ANALYZIN G THE PERFORMA NCE OF KNOWLED GE BASED
SYSTEMS
USI NG BAYESIA N NETWOR KS PR OJ EC T G UID E: Dr . K. R aja
TE AM M EMB ER S: S. L. K ok ila Ba i 20 053 832
K. M ana sa 20 053 834 N. M ano j 20 053 835
PROBLEM DESCRIPTION: A Knowledge Based System (KBS) can be said to be a computer system that is programmed to imitate human problem-solving with reference to a database of knowledge on a particular subject. The modeling techniques generally used in KBS such as the rule based reasoning, case based reasoning, etc have been observed to have certain disadvantages such as not being able to handle missing or unexpected information, users might allow cases to bias new problem solutions, a large case base adding to the challenge to storage and search processes, adaptation problem, i.e., modifying solutions of former similar cases to fit for a current problem etc. However, the Bayesian network which is a probabilistic graphical model that is based on Bayes Rule given as: For any two events, A and B, p(B|A) = p(A|B) x p(B) / p ( A ), where you read 'p(A)' as "the probability of A", and 'p(A|B)' as "the probability of A given that B has occurred". is known to possess certain properties such as readily being able to handle incomplete data sets, adaptable, is based on the best mathematical and statistical knowledge to date and models the scenario in a graphical model which makes it easy for one to understand the causal relationships between the variables. Hence, based upon the afore mentioned inherent properties of the Bayes Networks which overcome disadvantages of other modeling techniques, we propose to
incorporate the Bayesian network to support human decision making via a Knowledge Based System (KBS), and thereby analyze the performance of the Bayesian networks in KBS against those that are implemented using other models such as the CBR.
SYSTEMS
USI NG BAYESIA N NETWOR KS PR OJ EC T G UID E: Dr . K. R aja
TE AM M EMB ER S: S. L. K ok ila Ba i 20 053 832
K. M ana sa 20 053 834 N. M ano j 20 053 835
PROBLEM DESCRIPTION: A Knowledge Based System (KBS) can be said to be a computer system that is programmed to imitate human problem-solving with reference to a database of knowledge on a particular subject. The modeling techniques generally used in KBS such as the rule based reasoning, case based reasoning, etc have been observed to have certain disadvantages such as not being able to handle missing or unexpected information, users might allow cases to bias new problem solutions, a large case base adding to the challenge to storage and search processes, adaptation problem, i.e., modifying solutions of former similar cases to fit for a current problem etc. However, the Bayesian network which is a probabilistic graphical model that is based on Bayes Rule given as: For any two events, A and B, p(B|A) = p(A|B) x p(B) / p ( A ), where you read 'p(A)' as "the probability of A", and 'p(A|B)' as "the probability of A given that B has occurred". is known to possess certain properties such as readily being able to handle incomplete data sets, adaptable, is based on the best mathematical and statistical knowledge to date and models the scenario in a graphical model which makes it easy for one to understand the causal relationships between the variables. Hence, based upon the afore mentioned inherent properties of the Bayes Networks which overcome disadvantages of other modeling techniques, we propose to
incorporate the Bayesian network to support human decision making via a Knowledge Based System (KBS), and thereby analyze the performance of the Bayesian networks in KBS against those that are implemented using other models such as the CBR.
