Knowledge-Base including Advanced Intelligent Function by handing Inconplete Knowledge

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For expanding the capability of current knowledge-base systems by adding advanced artificial intelligence functions onto deductive inference mechanism, we carried out our research from the viewpoint of handling incomplete knowledge. Incomplete knowledge here means the knowledge with exceptions, hypothetical knowledge, defeasible knowledge, etc. We have selected a hypothetical reasoning system as our framework, since it can handle incomplete knowledge as hypothesis and is practically important framework applicable to many problems. The crucial problem with the hypothetical reasoning system is its slow inference speed because of its non-monotonicity nature. Thus we focused our research work on finding fast inference mechanism for the hypothetical reasoning.One achievement is a fast hypothetical reasoning method using inference-path network which contributes to avoid backtracking due to the inconsistency among hypothesis. Furthermore, in order to overcome the worst-case limit of exponential-order inference time, we have constructed several efficient hypothetical reasoning mechanisms. Namely, they are a hypothetical reasoning method using analogy, an experience-based learning mechanism for efficient hypothetical reasoning, logical knowledge compilation method foe efficient abductive hypothesis synthesis and a hypothetical reasoning method based on 0-1 integer programming with approximation.