KNET: Integrating Hypermedia and Bayesian Modeling
This work addresses the problem of knowledge engineering for expert systems, offering a tool for researchers and practitioners, but it is incremental as it combines existing hypermedia and Bayesian methods into a new architecture.
The authors tackled the challenge of constructing expert systems by developing KNET, a general-purpose shell that integrates HyperCard with Bayesian modeling to create a novel architecture for building decision networks, resulting in a system that provides a coherent probabilistic scheme for uncertainty management and a user-friendly interface for rapid prototyping.
KNET is a general-purpose shell for constructing expert systems based on belief networks and decision networks. Such networks serve as graphical representations for decision models, in which the knowledge engineer must define clearly the alternatives, states, preferences, and relationships that constitute a decision basis. KNET contains a knowledge-engineering core written in Object Pascal and an interface that tightly integrates HyperCard, a hypertext authoring tool for the Apple Macintosh computer, into a novel expert-system architecture. Hypertext and hypermedia have become increasingly important in the storage management, and retrieval of information. In broad terms, hypermedia deliver heterogeneous bits of information in dynamic, extensively cross-referenced packages. The resulting KNET system features a coherent probabilistic scheme for managing uncertainty, an objectoriented graphics editor for drawing and manipulating decision networks, and HyperCard's potential for quickly constructing flexible and friendly user interfaces. We envision KNET as a useful prototyping tool for our ongoing research on a variety of Bayesian reasoning problems, including tractable representation, inference, and explanation.