Default reasoning in semantic networks: a formalization of recognition and inheritance
Artificial Intelligence
Fundamental concepts of qualitative probabilistic networks
Artificial Intelligence
Representation requirements for supporting decision model formulation
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Integrating probabilistic, taxonomic and causal knowledge in abductive diagnosis
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
The representation of concepts in OWL
IJCAI'75 Proceedings of the 4th international joint conference on Artificial intelligence - Volume 1
A clash of intuitions: the current state of nonmonotonic multiple inheritance systems
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1
A hybrid framework for representing uncertain knowledge
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Using literature and data to learn Bayesian networks as clinical models of ovarian tumors
Artificial Intelligence in Medicine
| Hi-index | 0.00 |
Automated decision making is often complicated by the complexity of the knowledge involved. Much of this complexity arises from the context-sensitive variations of the underlying phenomena. We propose a framework for representing descriptive, context-sensitive knowledge. Our approach attempts to integrate categorical and uncertain knowledge in a network formalism. This paper outlines the basic representation constructs, examines their expressiveness and efficiency, and discusses the potential applications of the framework.