Representing Context-Sensitive Knowledge in a Network Formalism: A Preliminary Report
This work addresses the complexity of knowledge representation for automated decision systems, but it is a preliminary report with incremental contributions.
The authors tackled the problem of representing context-sensitive knowledge for automated decision making by proposing a network formalism that integrates categorical and uncertain knowledge, outlining its basic constructs, expressiveness, efficiency, and potential applications.
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.