Verification and validation of Bayesian knowledge-bases

✍ Scribed by Eugene Santos Jr.

• Publisher: Elsevier Science
• Year: 2001
• Tongue: English
• Weight: 218 KB
• Volume: 37
• Category: Article
• ISSN: 0169-023X
• DOI: 10.1016/s0169-023x(01)00011-8

✦ Synopsis

Knowledge-base V&V primarily addresses the question: Does my knowledge-base contain the right answer and can I arrive at it?'' One of the main goals of our work is to properly encapsulate the knowledge representation and allow the expert to work with manageable-sized chunks of the knowledge-base. This work develops a new methodology for the veri®cation and validation of Bayesian knowledge-bases that assists in constructing and testing such knowledge-bases. Assistance takes the form of ensuring that the knowledge is syntactically correct, correcting imperfect'' knowledge, and also identifying when the current knowledge-base is insucient as well as suggesting ways to resolve this insuciency. The basis of our approach is the use of probabilistic network models of knowledge. This provides a framework for formally de®ning and working on the problems of uncertainty in the knowledge-base.

In this paper, we examine the PESKI PESKI project which is concerned with assisting a human expert to build knowledgebased systems under uncertainty. We focus on how veri®cation and validation are currently achieved in PESKI PESKI. Ó 2001 Published by Elsevier Science B.V.