Concepts Learning based on Non-monotonic Logic

本研究旨在设计一个以非单调逻辑为基础的概念学习系统。我们使用TMS的dependency定向回溯和McCarthy的限制。我们描述了我们的系统的轮廓如下.识别不一致：当我们发现知识库中存在不一致时，为了使知识库保持一致，我们必须找出不一致的根源。依赖性导向的回溯用于此目的。由于我们将不一致定义为指定谓词的不相容约束，因此上述识别的搜索空间不会太大.在识别出不一致的来源之后，为了保持一致性，我们附加了一个新的谓词。通过限定新引入的谓词，我们得到了谓词的明确定义。换句话说，我们获得了更精确的知识库.如果我们把这些方法应用到伊萨层次结构中，新引入的谓词对应于一个新的概念。谓词的归属给了我们一个新概念的定义。

The purpose of this research is to design a concepts learning system using nonmonotonic logic. We use TMS's depebdency directed backtracking and McCarthy's circumscription especially. We describe the out-line of out system in the following.1. Identification of inconsistency: When we find inconsistency in a knowledge base, we must identify the souce of inconsistency in order to be the knowledge base consistent. The dependency directed backtracking is used for this purpose. Since we difine inconsistency as imcompatibility constraints for specified predicate, search space of the above described identification becomes not too large.2. After identification of the source of inconsistency, in order for consistency, we attach a new predicate. By circumscribing the newly introduced predicate, we obtain an explicit definition of the predicate. In other words, we obtain more precise knowledge base.3. If we apply these method to isa hierarchy, newly introduced predicate corresponds to a new concept. Corcumscription of the predicate gives us a definition of the new concept.