Uncertainty in Artificial Intelligence
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KNET: Integrating Hypermedia and Bayesian Modeling
R. Chavez, Gregory Cooper
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.
Pages: 49-54
PS Link:
PDF Link: /papers/88/p49-chavez.pdf
AUTHOR = "R. Chavez and Gregory Cooper",
TITLE = "KNET: Integrating Hypermedia and Bayesian Modeling",
BOOKTITLE = "Proceedings of the Fourth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-88)",
ADDRESS = "Corvallis, Oregon",
YEAR = "1988",
PAGES = "49--54"

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