Knowledge representation and inference in intelligent decision systems
Knowledge representation and inference in intelligent decision systems
Operations Research
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Intelligent decision systems
Artificial Intelligence - Special issue on knowledge representation
Probabilistic similarity networks
Probabilistic similarity networks
Computation and action under bounded resources
Computation and action under bounded resources
Representing context-sensitive knowledge in a network formalism: a preliminary report
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Utility-based categorization
Semantic Networks in Artificial Intelligence
Semantic Networks in Artificial Intelligence
Integrating probabilistic, taxonomic and causal knowledge in abductive diagnosis
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Problem formulation as the reduction of a decision model
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Dynamic construction of belief networks
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Evidential reasoning in semantic networks: a formal theory and its parallel implementation (inheritance, categorization, connectionism, knowledge representation)
A hybrid framework for representing uncertain knowledge
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
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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.