Generative Modeling with Failure in PRISM

被引：0
|
作者：
Sato, Taisuke [1 ]
Kameya, Yoshitaka [1 ]
Zhou, Neng-Fa
机构：
[1] JST, Tokyo Inst Technol CREST, Meguro Ku, Tokyo 1528552, Japan
来源：
19TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI-05) | 2005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：
PRISM is a logic-based Turing-complete symbolic-statistical modeling language with a built-in parameter learning routine. In this paper, we enhance the modeling power of PRISM by allowing general PRISM programs to fail in the generation process of observable events. Introducing failure extends the class of definable distributions but needs a generalization of the semantics of PRISM programs. We propose a three valued probabilistic semantics and show how failure enables us to pursue constraint-based modeling of complex statistical phenomena.
引用
收藏
页码：847 / 852
页数：6
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