A Dynamic Approach to Probabilistic Inference
This approach addresses the challenge of efficiently building Bayesian networks for applications requiring dynamic knowledge integration, though it appears incremental in nature.
The paper tackles the problem of constructing Bayesian networks dynamically by introducing a background knowledge base of schemata to separate general and specific knowledge, enabling the creation of networks for probabilistic inference.
In this paper we present a framework for dynamically constructing Bayesian networks. We introduce the notion of a background knowledge base of schemata, which is a collection of parameterized conditional probability statements. These schemata explicitly separate the general knowledge of properties an individual may have from the specific knowledge of particular individuals that may have these properties. Knowledge of individuals can be combined with this background knowledge to create Bayesian networks, which can then be used in any propagation scheme. We discuss the theory and assumptions necessary for the implementation of dynamic Bayesian networks, and indicate where our approach may be useful.