CONSTRUCTION OF BELIEF AND DECISION NETWORKS

TLDR

The paper proposes a representation and inference techniques for dynamically constructing probabilistic and decision‑theoretic network models. The authors develop a representation that implicitly encodes many possible model structures and a system that, given a query and current information, constructs a customized belief or decision network, using sensitivity analysis to guide model building. The study demonstrates the approach by discussing control issues and presenting examples of network construction.

Abstract

We describe a representation and set of inference techniques for the dynamic construction of probabilistic and decision‐theoretic models expressed as networks. In contrast to probabilistic reasoning schemes that rely on fixed models, we develop a representation that implicitly encodes a large number of possible model structures. Based on a particular query and state of information, the system constructs a customized belief net for that particular situation. We develop an interpretation of the network construction process in terms of the implicit networks encoded in the database. A companion method for constructing belief networks with decisions and values (decision networks) is also developed that uses sensitivity analysis to focus the model building process. Finally, we discuss some issues of control of model construction and describe examples of constructing networks.