Recursive learning method for knowledge-based planning system

Abstract

In the status selection planning system, which is a kind of knowledge-based planning system, the quality of the solution depends on the status selection rules. However, it is usually difficult to acquire useful knowledge from human experts. The learning method of a status selection rule using inductive learning is proposed. The status selection rules are divided into several stages according to the planning process. Gathering a training set and learning a part of the knowledge inductively are repeated one by one from the previous stage rules. From the result of application to a job-shop problem, the effectiveness of the proposed method is shown.