Compression-Driven Progress in Science

被引：0
|
作者：
Pape, Leo [1 ]
机构：
[1] Univ Utrecht, Utrecht, Netherlands
来源：
ARTRIFICIAL GENERAL INTELLIGENCE, AGI 2010 | 2010年 / 10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：
The construction of an artificial scientist, a machine that discovers and describes the general rules governing a variety of complex environments, can be considered an important challenge for artificial general intelligence. Recently, a computational framework for scientific investigation has been postulated in the theory of compression-driven progress. Here, I propose an implementation of an artificial scientist based on the compression principle, and explore the possibilities and challenges for its application in scientific research.
引用
收藏
页码：192 / 193
页数：2
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