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Knowledge Elicition From Multiple Experts Using Influence Diagrams

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dc.contributor.advisor Sadananda, Ramakoti
dc.contributor.author Saubhagya, Joshi Ram
dc.date.accessioned 2015-05-13T07:45:31Z
dc.date.available 2015-05-13T07:45:31Z
dc.date.issued 2000
dc.identifier.other AIT Thesis no.IM-00-7
dc.identifier.uri http://www.cs.ait.ac.th/xmlui/handle/123456789/751
dc.description.abstract On the basis of the model proposed by Rush and Wallace (1997) for elicitation of knowledge from multiple experts, I seek to implement the model and test the accuracy of the generated central network. The Multiple Expert using Influence Diagrams (MEID) is a technique for generation of an aggregate knowledge representation, called the central network, from several experts each representing ones knowledge in the form of an influence diagram. The main advantage of this technique is that it does not rely upon group interactions. The measures of the aggregate knowledge representation are the mean central network and the dispersion coefficient of the expert influence diagrams from the mean central network. The accuracy of the aggregate knowledge representation is measured by the confidence interval of distribution of the distance between the central network of the real experts and the central network of the bootstrapped samples of expert influence diagrams. In this thesis work, the goal is to test the adequacy and accuracy of the generated central network of real experts. The results of the tests show that the two parameters used to measure the aggregate knowledge representation are not sufficient as a measure. There is the need of another factor, which is the total number of vertices used by the experts. The number of vertices affects the confidence interval because the central network of the sample networks depends upon the vertices that are used by the experts. Using the total number of vertices used and the dispersion coefficient from the central network, we can judge the accuracy of the aggregate knowledge representation.
dc.publisher AIT en_US
dc.title Knowledge Elicition From Multiple Experts Using Influence Diagrams en_US
dc.type Thesis en_US


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