Designing Bayesian networks for highly expert-involved problem diagnosis domains

[摘要] Systems for diagnosing problems in highly complicated problem domains have been traditionally very difficult to design. Such problem diagnosis systems have often been restricted to the use of primarily rule-based methods for problem diagnosis in cases where machine learning for probabilistic methods has been made difficult by limited available training data. The probabilistic diagnostic methods that do not require a substantial amount of available training data usually require considerable expert involvement in design. This thesis proposes a model which balances the amount of expert involvement needed and the complexity of design in cases where training data for machine learning is limited. This model aims to use a variety of techniques and methods to translate, and augment, experts;; quantitative knowledge of their problem diagnosis domain into quantitative parameters for a Bayesian network model which can be used to design effective and efficient problem diagnosis systems.

[发布机构] Massachusetts Institute of Technology