Research on Computational Aids to Intellectual Discovery
Grant-in-Aid for Overseas Scientific Survey.
|Allocation Type||Single-year Grants|
|Research Institution||KEIO UNIVERSITY|
FURUKAWA Kouichi Gracuate school of Media and Governance, KEIO University, 大学院・政策・メディア研究科, 教授 (10245615)
IMAI Mutsumi Faculty of Environmental Information, KEIO University, 環境情報学部, 助手 (60255601)
TAKEFUJI Yoshiyasu Faculty of Environmental Information, KEIO University, 環境情報学部, 助教授 (40245618)
ISHIZAKI Shun Faculty of Environmental Information, KEIO University, 環境情報学部, 教授 (00245614)
ARIKAWA Setsuo Faculty of Science, KYUSHU University, 理学部, 教授 (40037221)
MIZOGUCHI Fumio Faculty of Science and Tcchnology, SCIENCE University of Tokyo, 理工学部, 教授 (50084463)
|Project Period (FY)
Completed(Fiscal Year 1994)
|Keywords||discovery in science and technology, / machine learning tools, / inductive logic programming, / higer order concepts learning, / neural network, / knowledge discovery in database, / molecular biology, / natural language processing|
The subject of the joint Anglo-Japanese research initiative is"Machine intelligence tools for discovery in science and technology."
In particular, we studied the use of three approaches :
*Inductive Logic Programming
*Neural network and related statistical learning techniques
In more detail, we investigated how to realize discovery and creation by computer using an Inductive Logic Programming system PROGOLdeveloped by Muggleton. As a result, we found that it is possible toachieve higher order concept learning and analogical reasoning by introducing new predicates in background knowledge in the input of PROGOL.Furthermore, we investigated how to discover knowledge in database and found the necessity to extract relevant information from database to adjust as an input to PROGOL.Also we found the importance of creating appropriate"negative examples"and"background knowledge". Then, we pursued the possibility to parallelize PROGOL in order to achieve high efficiency for dealing with large database. As an application of ILP,we picked up natural language processing. We investigated the Japanese correspondence to the verb"break"by investigating the degree of affinity to various nouns by cognitive psychological experiments and obtained a rule for predicting the affinity to a new noun. In scientific discovery, it is essential to be able to refute a whole
hypothsis space by just investigating finite number of hypothesis. We found it possible to achieve such refutability of hypothsis spaces for a fairly large class of hypothsis spaces.
Research Output (9results)