  Probabilistic conceptual network is a knowledge representation scheme
designed for reasoning about concepts and categorical abstractions in
utility-based categorization. The scheme combines the formalisms of abstraction
and inheritance hierarchies from artificial intelligence, and probabilistic
networks from decision analysis. It provides a common framework for
representing conceptual knowledge, hierarchical knowledge, and uncertainty. It
facilitates dynamic construction of categorization decision models at varying
levels of abstraction. The scheme is applied to an automated machining problem
for reasoning about the state of the machine at varying levels of abstraction
in support of actions for maintaining competitiveness of the plant.
