Knowledge-Based Probabilistic Logic Learning

被引：0

作者：

Odom, Phillip ^{[1
]}

Khot, Tushar ^{[2
]}

Porter, Reid ^{[3
]}

Natarajan, Sriraam ^{[4
]}

机构：

[1] Indiana Univ, Dept Comp Sci, Bloomington, IN 47405 USA

[2] Univ Wisconsin, Dept Comp Sci, Madison, WI 53706 USA

[3] Los Alamos Natl Lab, Intelligence & Space Res Div, Los Alamos, NM USA

[4] Indiana Univ, Sch Informat & Comp, Bloomington, IN USA

来源：

PROCEEDINGS OF THE TWENTY-NINTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE | 2015年

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：

Advice giving has been long explored in artificial intelligence to build robust learning algorithms. We consider advice giving in relational domains where the noise is systematic. The advice is provided as logical statements that are then explicitly considered by the learning algorithm at every update. Our empirical evidence proves that human advice can effectively accelerate learning in noisy structured domains where so far humans have been merely used as labelers or as designers of initial structure of the model.

引用

页码：3564 / 3570

页数：7

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