A Generic Planning Method with Knowledge Acquisition Function

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We devised several kinds of automatic learning method for planning knowledge which are based on Status Selection Planning Method that we had proposed. They enable to constract a planning systems which can deal with generic planning problems. ID3, which is one of typical inductive learning methods, was applied to planning knowledge acquisition, and format of training data, which enables to learn planning knowledge was proposed. Because the inductive learning have a problem of how to gather a great deal of training data, recursive type training data gathering method was proposed. In the method, knowledge is divided into several stages of knowledge, gathering training data and application of inductive learning are recursively repreated one by one from the latest stage knowledge, in which it is easy to gathering training data. These methods were applied to flow shop problems and job shop problems. From the experimentation, it was confirmed that these methods could generate the superior knowledge than human knowledge. The automatic learning methods for planning knowledge decreases time for planning, so that it is obvious that these method is effective for CIM,in which short time planning for a large scale problem is necessary.As mention above, by introducing automatic learning feature of planning knowledge, we actualized a generic planning architecture which supported planning comprehensively.