中文摘要：

研究了一致性规划任务信念状态空间的表示方法。针对一致性有限域表示（CPT-FDR）算法在任务生成阶段选择状态变量的不足,提出了一种基于初始状态中文字相容互斥的状态变量选择算法——MECV算法。CPT-FDR未考虑初始信念状态中文字的互斥性,产生冗余的编码信息,降低了编码的效率。MECV算法利用有用正负文字构造新的未覆盖事实集,提取初始信念状态中处于不同世界状态的文字组成互斥组,再编码状态变量。实验结果表明该算法能有效地压缩信念状态空间。

英文摘要：

This study focused on the representation of the belief states in conformant planning tasks. The state variables selection algorithm in the process of CPT-FDR generation is not enough to deal with the mutex conditions. This paper put forward a new method named MECV（ mutually-exclusive-choose-variables）. CPT-FDR did not think over the mutually-exclusive between initial belief states. It generated redundant information which reduced the efficiency of encoding. First,MECV used the useful positive and negative literal to make a new uncovered fact set. And then it took out the mutually exclusive literal from different real state which in the initial belief states to make a mutually-exclusive-group. Finally,it used the new uncovered fact set and mutually-exclusive-group encoding state variables. The outcomes of comparative experiments validate the effectiveness of the new algorithm.