| Project/Area Number |
15300269
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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 |
Educational technology
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| Research Institution | National University Corporation Tokyo University of Agriculture and Technology |
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Principal Investigator |
KOTANI Yoshiyuki National University Corporation Tokyo University of Agriculture and Technology, Institute of Symbiotic Science and Technology, Professor, 大学院・共生科学技術研究部, 教授 (20111627)
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| Co-Investigator(Kenkyū-buntansha) |
INUI Nobuo National University Corporation Tokyo University of Agriculture and Technology, Institute of Symbiotic Science and Technology, Research Associate, 大学院・共生科学技術研究部, 助手 (20236384)
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| Project Period (FY) |
2003 – 2004
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| Project Status |
Completed (Fiscal Year 2004)
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| Budget Amount *help |
¥5,900,000 (Direct Cost: ¥5,900,000)
Fiscal Year 2004: ¥2,100,000 (Direct Cost: ¥2,100,000)
Fiscal Year 2003: ¥3,800,000 (Direct Cost: ¥3,800,000)
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| Keywords | creativity / randomness / story making / Shiritori / reasoning puzzle / conversation flow / conversation response / decision tree / 物語作成 / 創造性開発 / マルチエージェント / 覆面算 / ランダム生成 |
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| Research Abstract |
It is said that creativity is originated in human beings. This study aims to realize such creativity on machines.
We assume, in this study, that the creativity is a concept occurred from combinations of well-known knowledge. In other words, randomness is a key idea for machine creativity. We apply this idea to our story making system.
We have had two types of story making systems. One asks queries to users, and a story is created from human replies. Another uses thesaurus and outputs a plot of a story without human help.
With these systems, we have shown a meaning of user's help and how it is powerful for the implementation of machine creativity.
For a related study, we have studied a "Shiritori" problem in Japanese. Shiritori is a word game played by more than two players. Then, we have proposed two types of algorithms for Shiritori ; one outputs the longest word chain that has the maximum words, and another outputs the longest word chain that has the maximum characters.
In addition, we have proposed an algorithm for making a concept network from relations of words and randomness, a text segmentation method by words connection, a learning algorithm of an evaluate function on a multi-agent environment, and so on.
Machine creativity can be realized by an automatic conversation system. We have also proposed a reply selection system, word selection method via decision tree learning and a conversation analyzing system.
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