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Research on Knowledge Discovery based on Consequence Finding

基于结果发现的知识发现研究

基本信息

批准号：
14380164
负责人：
INOUE Katsumi
金额：
$ 4.03万
依托单位：
National Institute of Informatics (2004)Kobe University (2002-2003)
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
2002
资助国家：
日本
起止时间：
2002 至 2004
项目状态：
已结题

项目摘要

In this research, we developed new methods of knowledge discovery under incomplete knowledge. The proposed systems compute abductive and inductive hypotheses based on consequence-finding procedures. Research results can be summarized as the following three items.1. Efficient computation for consequence findingWe adopted SOL resolution by Inoue as a consequence-finding procedure and made it more efficient. In particular, we developed SOL-S(Г) tableaux for efficient speculative computation in multi-agent systems and default reasoning. Moreover, we re-implemented SOL tableaux in Java, and developed a faster consequence-finding procedure SOLAR (SOL for Advanced Reasoning).2. Basic theories for consequence finding and knowledge discoveryWe proved that SOL resolution is complete for answer extraction in first-order clausal theories. This is a solution of an open problem for answer completeness in a connection tableaux format. We also considered a hypothesis-finding procedure based on consequence finding (called CF-induction), and found a complete method for generalization in CF-induction. Moreover, we established a unified theory for induction, which combines explanatory induction and descriptive induction. This inductive formalization is based on circumscription, and uses SOL resolution and CF-induction for computing hypotheses.3. Evaluation and applications of hvpothesis-finding proceduresWe applied SOL resolution to bioinformatics, and considers the use of extended abduction, which enables us to remove hypotheses as well as addition of them.

在本研究中，我们开发了不完全知识下的知识发现新方法。所提出的系统基于结果发现程序计算溯因和归纳假设。研究结果可以概括为以下三点。我们采用Inoue的SOL解析作为结果查找过程，使其更加高效。特别是，我们开发了SOL-S（Г） tableaux，用于在多智能体系统和默认推理中进行有效的推测计算。此外，我们在Java中重新实现了SOL表，并开发了一个更快的结果查找过程SOLAR （SOL for Advanced Reasoning）。结果发现和知识发现的基本理论我们证明了一阶子句理论中答案提取的SOL解析是完整的。这是一个在连接表格式中回答完整性的开放问题的解决方案。我们还考虑了一种基于结果发现的假设发现过程（称为cf -归纳），并找到了一种完整的cf -归纳泛化方法。建立了解释归纳法与描述归纳法相结合的统一归纳法理论。这种归纳形式化是基于限制的，并使用SOL解析和cf归纳来计算假设。假设发现程序的评估和应用我们将SOL分辨率应用于生物信息学，并考虑使用扩展溯因法，这使我们能够删除假设以及添加假设。