出版社：The Japanese Society for Artificial Intelligence
摘要：In this paper, we discuss a method to dynamically determine the generality of the target concept in a class hierarchy, when learning default rules, i.e., rules including exceptions with Inductive Logic Programming (ILP). The ILP system for default rules has to learn both the target concept and its opposite, if it is based on a three valued setting, in which we clearly discriminate among the three values: what is true, what is false, and what is unknown. Thus in order to learn rules which holds as generally as possible in a class hierarchy implicitly existing in given examples, we should give a higher priority to the concept which is more general, or covers more examples than does the other in the hierarchy. For this purpose, our method first finds out the general rule from a set of candidate rules independently of the concept it defines. Then the body of the rule can be viewed as the description defining the most general class in the hierarchy. Therefore, according to the ratio of positive examples it covers, we can determine which of the concepts, the target one or its opposite, is more general, and dynamically change the head of the rule to the negative literal if the latter concept is more general. In this paper, we formalize this method as a new ILP system, GREX, and discuss it with some examples.
