Representing Context-Sensitive Knowledge in a Network Formalism: A Preliminary Report
arXiv:1303.5414
Abstract
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.
Appears in Proceedings of the Eighth Conference on Uncertainty in Artificial Intelligence (UAI1992)