An information-theoretic approach for the quantification of relevance

被引：0
|
作者：
Polani, Daniel [1 ]
Martinetz, Thomas [1 ]
Kim, Jan [1 ]
机构：
[1] Med Univ Lubeck, Inst Neuro & Bioinformat, Lubeck, Germany
来源：
ADVANCES IN ARTIFICIAL LIFE | 2001年 / 2159卷
关键词：
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：
We propose a concept for a Shannon-type quantification of information relevant to a decision unit or agent. The proposed measure is operational, calf - at least in principle - be calculated for a given system and has an immediate interpretation as an information quantity. Its use as a natural framework for the study of sensor evolution is discussed.
引用
收藏
页码：704 / 713
页数：10
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