| Co-Investigator(Kenkyū-buntansha) |
久保 孝富 奈良先端科学技術大学院大学, 先端科学技術研究科, 准教授 (20631550)
松香 敏彦 千葉大学, 大学院人文科学研究院, 教授 (30466693)
田口 亮 名古屋工業大学, 工学(系)研究科(研究院), 准教授 (70508415)
岩橋 直人 岡山県立大学, 情報工学部, 教授 (90394999)
小林 一郎 お茶の水女子大学, 基幹研究院, 教授 (60281440)
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| Budget Amount *help |
¥67,470,000 (Direct Cost: ¥51,900,000、Indirect Cost: ¥15,570,000)
Fiscal Year 2020: ¥13,650,000 (Direct Cost: ¥10,500,000、Indirect Cost: ¥3,150,000)
Fiscal Year 2019: ¥13,650,000 (Direct Cost: ¥10,500,000、Indirect Cost: ¥3,150,000)
Fiscal Year 2018: ¥13,650,000 (Direct Cost: ¥10,500,000、Indirect Cost: ¥3,150,000)
Fiscal Year 2017: ¥13,650,000 (Direct Cost: ¥10,500,000、Indirect Cost: ¥3,150,000)
Fiscal Year 2016: ¥12,870,000 (Direct Cost: ¥9,900,000、Indirect Cost: ¥2,970,000)
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| Outline of Final Research Achievements |
This study aimed to (1) develop a computational theory of an internal model that represents the computational processes of double-articulation analysis and dynamic category formation in the human brain, (2) clarify the computational processes of them in a human brain, and (3) create robots that achieve autonomous language acquisition and motor learning by accelerating joint research within the innovative research area. We established a method of simultaneous learning of Bayesian double-articulation analysis and object and place categories. A segmentation method of human actions and a relative place concept formation method were also developed. We also proposed an idea called the whole-brain probabilistic generative model for understanding and constructing an entire-brain cognitive system combining generative models.
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