Learned models for continuous planning

被引：0
|
作者：
Schmill, MD [1 ]
Oates, T [1 ]
Cohen, PR [1 ]
机构：
[1] Univ Massachusetts, Dept Comp Sci, LGRC, Amherst, MA 01003 USA
来源：
ARTIFICIAL INTELLIGENCE AND STATISTICS 99, PROCEEDINGS | 1999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：
We are interested in the nature of activity structured behavior of nontrivial duration in intelligent agents. We believe that the development of activity is a continual process in which simpler activities are composed, via planning, to form more sophisticated ones in a hierarchical fashion. The success or failure of a planner depends on its models of the environment, and its ability to implement its plans in the world. We describe an approach to generating dynamical models of activity from real-world experiences and explain how they can be applied towards planning in a continuous state space.
引用
收藏
页码：278 / 282
页数：5
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