| Project/Area Number |
23K11262
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 61040:Soft computing-related
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| Research Institution | Shinshu University |
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Principal Investigator |
Arnold Solvi 信州大学, 工学部, 准教授(特定雇用) (80764935)
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| Co-Investigator(Kenkyū-buntansha) |
有田 隆也 名古屋大学, 情報学研究科, 教授 (40202759)
鈴木 麗璽 名古屋大学, 情報学研究科, 准教授 (20362296)
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| Project Period (FY) |
2023-04-01 – 2026-03-31
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| Project Status |
Granted (Fiscal Year 2023)
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| Budget Amount *help |
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2025: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2024: ¥2,990,000 (Direct Cost: ¥2,300,000、Indirect Cost: ¥690,000)
Fiscal Year 2023: ¥520,000 (Direct Cost: ¥400,000、Indirect Cost: ¥120,000)
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| Keywords | 人工知能 / ニューラルネットワーク / 人工生命 / ボールドウィン効果 / 機械学習 |
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| Outline of Research at the Start |
Learning is a key aspect of intelligence. In contrast to AI, humans can learn efficiently from limited experience. Human cognition is evolutionarily specialised to learn important tasks (e.g. learning to walk, acquiring language) rapidly. This is hypothesised to be a core factor in cognitive evolution. We computationally model the evolution of learning ability using neural networks, with a focus on such specialisation. Our goals are 1) to explore how we can make AI learning more human-like, and 2) to gain new insights in the evolution of intelligence in nature.
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