Learning Cellular Automata Represented as Logic Programs
| Project/Area Number |
26540122
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| Research Category |
Grant-in-Aid for Challenging Exploratory Research
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| Allocation Type | Multi-year Fund |
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| Research Field |
Intelligent informatics
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| Research Institution | National Institute of Informatics |
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Principal Investigator |
INOUE KATSUMI 国立情報学研究所, 情報学プリンシプル研究系, 教授 (10252321)
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| Co-Investigator(Kenkyū-buntansha) |
SAKAMA CHIAKI 和歌山大学, システム工学部, 教授 (20273873)
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| Project Period (FY) |
2014-04-01 – 2016-03-31
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| Project Status |
Completed (Fiscal Year 2015)
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| Budget Amount *help |
¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
Fiscal Year 2015: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2014: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Keywords | 機械学習 / セルオートマトン / ブーリアンネットワーク / 状態遷移 / 行動規則学習 / 論理発見 / 遺伝子制御ネットワーク学習 |
|---|
| Outline of Final Research Achievements |
Learning from Interpretation Transition (LFIT) is a method of unsupervised learning, which learns the dynamics of a system from observed time-series data. LFIT has been developed in three ways: (1) Learning memory-less systems from 1-step state transitions, which contains three different implementations to learn the state transition rules, that is, (a) generalization based on the resolution principle, (b) extension of the binary decision diagram (BDD), and (c) least specialization that guarantees the minimality of learned rules; (2) Learning systems with memory (or delay), which can learn Markov(k) systems that depend on k previous states; and (3) Learning nondeterministic and probabilistic systems, which can work for noisy data.
These three learning algorithms have been implemented and evaluated with bioinformatics data to construct gene regulatory networks. LFIT has also been applied to identification of cellular automata, learning robot planning rules, and learning logics.
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Report
(3 results)
Research Products
(28 results)
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[Journal Article] Inductive Logic Programming: Challenges2016
Author(s)
Katsumi Inoue, Hayato Ohwada, Akihiro Yamamoto
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Journal Title
In: Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI-16; Phoenix, Arizona, USA, February 14, 2016)
Volume: -
Pages: 4330-4332
Related Report
Peer Reviewed / Open Access
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[Journal Article] Can Machine Learn Logics?2015
Author(s)
Chiaki Sakama, Katsumi Inoue
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Journal Title
Artificial General Intelligence: Proceedings of the 8th International Conference , Lecture Notes in Artificial Intelligence
Volume: 9205
Pages: 341-351
DOI
ISBN
9783319213644, 9783319213651
Related Report
Peer Reviewed
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[Presentation] Panel: ILP 25 Years2015
Author(s)
Stephen Muggleton, Fabrizio Riguzzi, Filip Zelezny, Gerson Zaverucha, Jesse Davis, Katsumi Inoue, and Taisuke Sato
Organizer
he 25th International Conference on Inductive Logic Programming
Place of Presentation
京都大学
Year and Date
2015-08-22
Related Report
Int'l Joint Research
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