摘要
近年来行动推理的研究成为人工智能领域的热门问题,而结果问题是目前行动推理研究的核心问题之一.该文针对许多行动推理系统不能处理循环因果关系的问题,提出了解决方法.基于适当修改后的McCain和Turner的因果理论,该文提出了一种能处理循环依赖的理论转化方法.转化后的因果理论消除了循环依赖,而且可以采用单调推理方法.基于因果闭包语义,证明了转化前后的因果理论具有相同的模型.当因果关系不存在循环依赖时,该文方法得到与McCain,Turner方法(1997)相同的结果.
Intelligent behaviors of humans require the ability to predict the result of actions. McCarthy holds the view that reasoning about action plays an essential role in commonsense reasoning. In recent years, reasoning about action is a popular issue in AI domain and becomes a discussion subject in IJCAI for several years. Ramification problem is currently one of the central problems is reasoning about action. This paper describes some background and the main task of reasoning about action, recapitulates the ramification problem and points out the difficulty in it. Most of the current methods of reasoning about action cannot handle cyclic causal relation. This paper presents a solution to this problem. Firstly, based on modified causal theory of McCain and Turner, it puts forward an automatable procedure that transforms a syntactically restricted causal theory into a monotonic one without cyclic causality. Next, it proves the correctness of the procedure under causal closure semantic by showing that an interpretation is the model of the transformed theory if and only if it is the model of original theory. This paper also proves that the method yields the same results as that of McCain and Turner (1997) when the theory is stratified.
出处
《计算机学报》
EI
CSCD
北大核心
2005年第5期829-837,共9页
Chinese Journal of Computers
基金
国家自然科学基金(60003010)
江苏省自然科学资金(BK2004079)资助.~~
关键词
因果关系
结果问题
行动推理
非单调推理
规划
因果理论
Inference engines
Intelligent agents
Knowledge representation
Mathematical models
Mathematical transformations
Planning
Semantics
Theorem proving
