Using Conditional Random Fields for Decision-Theoretic Planning

被引：0
|
作者：
Ardis, Paul A. [1 ]
Brown, Christopher M. [1 ]
机构：
[1] Univ Rochester, Rochester, NY 14627 USA
来源：
MODELING DECISIONS FOR ARTIFICIAL INTELLIGENCE, PROCEEDINGS | 2009年 / 5861卷
关键词：
Utility and Decision Theory; Graphical Modeling;
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 propose a means of extending Conditional Random Field modeling to decision-theoretic planning where valuation is dependent upon fully-observable factors. Representation is discussed, and a comparison with existing decision problem methodologies is presented. Included are exact and inexact message passing schemes for policy making, examples of decision making in practice, extensions to solving general decision problems, and suggestions for future use.
引用
收藏
页码：126 / 136
页数：11
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