Improving agent learning through rule analysis

被引：0

作者：

Boicu, C ^{[1
]}

Tecuci, G ^{[1
]}

Boicu, M ^{[1
]}

机构：

[1] George Mason Univ, Dept Comp Sci, Learning Agents Ctr, Fairfax, VA 22030 USA

来源：

ICAI '05: PROCEEDINGS OF THE 2005 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOLS 1 AND 2 | 2005年

关键词：

knowledge acquisition; machine learning; rule-based expert systems;

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：

This paper addresses the problem of improving the process by which an agent learns problem solving rules from a subject matter expert. It presents two complementary rule analysis methods that discover when a rule was learned from incomplete explanations of an example, guiding the expert to provide additional explanations. One method performs a structural analysis of the learned rule, while the other method analyles the possible rule instantiations. Both methods have been implemented in the Disciple-RKF learning agent and have been tested both in an automatic framework and during two knowledge acquisition experiments performed with subject matter experts at the US Army War College.

引用

页码：491 / 497

页数：7