Probabilistic Conceptual Network: A Belief Representation Scheme for Utility-Based Categorization
This work addresses the need for integrated knowledge representation in AI and decision analysis, specifically for utility-based categorization in domains like automated machining, but it appears incremental as it combines existing formalisms without claiming major breakthroughs.
The paper tackles the problem of representing conceptual knowledge and uncertainty for utility-based categorization by introducing a probabilistic conceptual network that combines AI abstraction hierarchies with probabilistic networks, and demonstrates its application in an automated machining problem for reasoning about machine states at varying abstraction levels to support plant competitiveness.
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.