出版社：The Japanese Society for Artificial Intelligence
摘要：It is important to increase computation efficiency of a system at a low cost while holding the system correct. For this realization, addition of new and efficient equivalent transformation (ET) rules, whose correctness is assured, is useful. As long as correct ET rules are added to a correct system, the computation result of the system is always correct. Improvement of rules is promoted further by improvement of data structure.In this paper, we improve data structure by introducing interval variables into the usual term domain and add two correct and efficient ET rules, which are promoted by introduction of interval variables, for member constraints on interval variables. These rules are the candidate elimination rule and the common pattern specialization rule. We show by an experiment that computation efficiency is increased by using these rules.
关键词：interval variable ; member constraint ; equivalent transformation ; problem solving
