What next for learning in AI planning?

被引：0
作者：
Zimmerman, T [1 ]
Kambhampati, S [1 ]
机构：
[1] Arizona State Univ, Dept Comp Sci & Engn, Tempe, AZ 85287 USA
来源：
IC-AI'2001: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOLS I-III | 2001年
关键词：
planning; learning; inductive; analytical; survey;
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 reports on a comprehensive survey of research work related to machine learning as applied to Ad planning over the past 15 years. Major research contributions are characterized broadly by learning method and then into descriptive subcategories. Survey results reveal learning techniques that have been extensively applied and a number that have received scant attention. We extend the survey analysis to suggest promising avenues for future research in learning based on both previous experience and current needs in the planning community.
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收藏
页码：1030 / 1036
页数：7
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