Uncertainty Modelling in Diagnostic Systems: An Adaptive Solution

Piccinelli, Giacomo

HP Development Company

关键词: diagnosis;    case-based reasoning;    artificial intelligence;    information retrieval;    knowledge management;   

RP-ID  :  HPL-98-37

学科分类：计算机科学（综合）

美国|英语

来源: HP Labs

【 摘 要 】

Uncertainty permeates the entire diagnostic process and its management is a fundamental issue in actual diagnostic systems. The type of information we can model about the context in which a problem occurred is crucial. The main components pictured in the definition of a context are observations (facts) but we argue that data on relevance and confidence may add precious information. Focusing on case-based reasoning (CBR) paradigm, we present a model in which relevance and uncertainty become fundamental and dynamic components of both diagnostic knowledge and processes: fuzzy sets are the theoretic base of the model. A conversational CBR shell implementing nearest- neighbour (NN) retrieval mechanisms has been developed in order to test our proposal in terms of case- retrieval precision and we discuss the results obtained in some experiments. The "knowledge level" impact of our proposal is also discussed. 10 Pages