Knowledge-Base including Advanced Intelligent Function by handing Inconplete Knowledge

为了通过在演绎推理机制中加入先进的人工智能功能来扩展现有知识库系统的能力，我们从处理不完备知识的角度进行了研究。这里的不完全知识指的是有例外的知识、假设知识、可废止知识等。我们选择了一个假设推理系统作为我们的框架，因为它可以处理不完全知识作为假设，并且是适用于许多问题的实际重要框架。假设推理系统的关键问题是，由于其非单调性，其推理速度很慢。其中一个成果就是利用推理路径网络实现了一种快速的假设推理方法，避免了由于假设之间的不一致而导致的回溯。此外，为了克服指数级推理时间的最坏情况限制，我们构造了几种有效的假设推理机制。它们是一种基于类比的假设推理方法、一种基于经验的有效假设推理机制、一种用于有效综合溯因假设的逻辑知识编译方法和一种基于0-1带逼近整数规划的假设推理方法。

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.