2004 Fiscal Year Final Research Report Summary
Research on Knowledge Discovery based on Consequence Finding
| Project/Area Number |
14380164
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | National Institute of Informatics (2004)
Kobe University (2002-2003) |
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Principal Investigator |
INOUE Katsumi NII, Foundations of Informatics Research Division, Professor, 情報学基礎研究系, 教授 (10252321)
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| Co-Investigator(Kenkyū-buntansha) |
IWANUMA Koji Yamanashi Univ., Dept.Computer and Media Eng, Professor, 大学院・医学工学総合研究部, 教授 (30176557)
NABESHIMA Hidetomo Yamanashi Univ., Dept. Computer and Media Eng., Assistant Professor, 大学院・医学工学総合研究部, 助手 (10334848)
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| Project Period (FY) |
2002 – 2004
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| Keywords | consequence finding / consequence finding procedure / SOL resolution / knowledge discovery / induction / abduction / automated deduction / tableaux method |
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| Research Abstract |
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 finding
We 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 discovery
We 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 procedures
We applied SOL resolution to bioinformatics, and considers the use of extended abduction, which enables us to remove hypotheses as well as addition of them.
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