During the past decades, many methods have been developed for the creation of Knowledge-Based Systems (KBS). For these methods, probabilistic networks have shown to be an important tool to work with probability-measured uncertainty. However, quality of probabilistic networks depends on a correct knowledge acquisition and modelation. KAMET is a model-based methodology designed to manage knowledge acquisition from multiple knowledge sources [1] that leads to a graphical model that represents causal relations. Up to now, all inference methods developed for these models are rule-based, and therefore eliminate most of the probabilistic information. We present a way to combine the benefits of Bayesian networks and KAMET, and reduce their problems. To achieve this, we show a transformation that generates directed acyclic graphs, the basic structure of Bayesian networks [2], and conditional probability tables, from KAMET models. Thus, inference methods for probabilistic networks may be used in KAMET models.
展开▼
