中文摘要：

偏好处理是人工智能中的一个重要的研究内容,条件偏好网（conditional preference networks,CP-nets）是一个带标记的有向图,它编码相关变量之间的偏好关系.作为一种简单直观的图形偏好表示工具,却很少有工作对CP-nets的结构进行研究.研究CP-nets的结构,提出了基于G方检验对CP-nets进行结构学习的算法,并给出算法的时间复杂度为O（n·2n）.作为一种对数似然比检验方法,G方检验特别适合于判断变量之间的因果关系.由于CP-nets的核心概念是条件偏好无关,因此利用G方检验可有效地实现CP-nets的结构学习.通过构造G方检验的统计量,在给定的成对比较样本集中,执行零假设检验,从而依次求出每个顶点的父亲集,进而得到CP-nets的结构.最后,通过随机生成的模拟数据,验证了所提出算法的有效性.与相关CP-nets的学习算法对比,本文提出的方法具有被动的,离线的,和基于统计学习的特征.

英文摘要：

Preference handling is one of the important research contents in the field of artificial intelligence.CP-nets（conditional preference networks）as a popular language capable of representing ordinal preference relations in a compact and structured manner,has received increasing attentions in recent years.A CP-net is an annotated directed graph that encodes the preference relationships among variables of interest.The conditional preference networks is a simple and intuitive graphical tool for representing conditional ceteris paribus preference statements over the values of a set of variables.But there are very few works that address the problems of structure learning of CP-nets.In this paper,the structure learning of CP-nets is studied,in particular,and learning algorithm based on the G2 test to learn the structure of CP-nets are discussed detailedly.Furthermore,it is proved that the time complexity of this algorithm is O（n·2n）.As a log likehood ratio testing approach,G2 testing is especially suitable for testing the causal relationship between variables.Conditional preference independence is the core concept of CP-nets,and using G2 test can learn the CP-nets structure effectively.By constructing G2 test statistics,and performing null hypothesis test fromthe given pairwise comparisons samples,we can get the parents of each node,therefore obtain the structure of CPnets.Finally,the effective of proposed algorithm is verified by random generated simulation data.Compared with other CP-nets learning approaches,the approach proposed in the paper has the characteristic of passive,offline and essence of statistical learning.