证据推理系统的扩展框架

[1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence Pub Date : 1990-11-06 DOI:10.1109/TAI.1990.130429

Weiru Liu, Jun-Hyeok Hong, M. McTear, J. Hughes

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引用次数: 12

摘要

基于Dempster-Shafer (D-S)证据理论和G. Yen(1989)对该理论的扩展，作者提出了通过证据映射来表示启发式知识的方法，并通过使用Shafter的分区技术对框架进行分区来汇集复杂框架中的质量分布。作者将Yen的模型从贝叶斯概率论推广到D-S证据理论。在此广义模型的基础上，提出了一种基于半图方法描述启发式知识的证据推理系统扩展框架。这种方法的优点是既避免了图的复杂性，又不失去图的显式性。该扩展框架可广泛用于构建专家系统。

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An extended framework for evidential reasoning systems

Based on the Dempster-Shafer (D-S) theory of evidence and G. Yen's (1989), extension of the theory, the authors propose approaches to representing heuristic knowledge by evidential mapping and pooling the mass distribution in a complex frame by partitioning that frame using Shafter's partition technique. The authors have generalized Yen's model from Bayesian probability theory to the D-S theory of evidence. Based on such a generalized model, an extended framework for evidential reasoning systems is briefly specified in which a semi-graph method is used to describe the heuristic knowledge. The advantage of such a method is that it can avoid the complexity of graphs without losing the explicitness of graphs. The extended framework can be widely used to build expert systems.<>

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来源期刊

[1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence

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