Affiliation: | Department of Computer Science and Engineering, University of Connecticut, UTEB, 191 Auditorium Rd, U155 Storrs, CT 06269-3155, USA |
Abstract: | 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 verification 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 insufficient as well as suggesting ways to resolve this insufficiency. The basis of our approach is the use of probabilistic network models of knowledge. This provides a framework for formally defining and working on the problems of uncertainty in the knowledge-base. In this paper, we examine the
project which is concerned with assisting a human expert to build knowledge-based systems under uncertainty. We focus on how verification and validation are currently achieved in
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