Probabilistic Conceptual Network: A Belief Representation Scheme for Utility-Based Categorization

Probabilistic conceptual network is a knowl­ edge representation scheme designed for reasoning about concepts and categorical abstractions in utility-based categorization. The scheme combines the formalisms of ab­ straction and inheritance hierarchies from artificial intelligence, and probabilistic net­ works from decision analysis. It provides a common framework for representing con­ ceptual knowledge, hierarchical knowledge, and uncertainty. It facilitates dynamic con­ struction 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.