A computing anticipatory system has to reason about actions as fast as possible in order to decide its next actions anticipatorily. We have implemented an action reasoning engine with general-purpose for various computing anticipatory systems and performed some experiments with the action reasoning engine and empirical knowledge represented quantitatively. Our experiments showed that the quantitative approach is very time-consuming and therefore it is not suitable to computing anticipatory systems with high reliability and high security requirements.This paper presents a qualitative approach for reasoning about actions fast. We show that the qualitative approach is more effective than the quantitative approach and therefore it is more suitable to computing anticipatory systems with high reliability and high security requirements.