Fuzzy set theory and uncertainty in case-based reasoning

被引：0
|
作者：
Weber, R. [1 ]
机构：
[1] Drexel Univ, Coll Informat Sci & Technol, Philadelphia, PA 19104 USA
来源：
ENGINEERING INTELLIGENT SYSTEMS FOR ELECTRICAL ENGINEERING AND COMMUNICATIONS | 2006年 / 14卷 / 03期
关键词：
case-based reasoning; fuzzy set theory; real-world applications; theory of typicality; uncertainty;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Case-based reasoning (CBR) is a reasoning methodology that relies on previous experiences, making it well suited to various real world application domains. When we use CBR to solve real world problems, its inherent uncertainty tends to propagate and may be detrimental to the system's quality. Consequently, the quality of a CBR system's outcome tends to increase as we address its uncertainty. This article examines approaches based on fuzzy set theory that manage the uncertainty originated in CBR systems and describes in detail a method based to address the uncertainty originated in the CBR assumption that similar problems have similar solutions.
引用
收藏
页码：121 / 136
页数：16
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