基于分层因果关系的定性推理方法

定性推理能为信息不完全的复杂系统产生行为预测,但推理分支可能出现组合爆炸,限制了它的应用.传统的算法不仅产生无法控制的推理分支,并且占用了大量的内存,不仅使用户不易理解推理的结果,而且甚至导致推理的失败.在LSIM方法的基础上提出了一种基于分层因果关系的定性推理方法LCQR(layered causal qualitative reasoning),旨在解决这一问题,使提取出的变量间的因果关系层次化,并应用于定性推理,取得了较满意的结果.

A Method of Qualitative Reasoning Based on Layered Causal Relation

Qualitative reasoning can predict the system behavior of complicated system with incompleted knowledge, but the combinatorial explosion of reasoning branches limits its application. Traditional algorithms not only produce intractable reasoning branches, but also occupy a lot of memory space. It makes the result of reasoning difficult to be understood, and sometimes it even leads to failure. In this paper, on the base of LSIM, a method of qualitative reasoning based on LCQR(layered causal qualitative reasoning) is proposed to solve this problem. LCQR extracts the causal relation between variables, layers it and utilizes it in qualitative reasoning. The result of LCQR is satisfying.