Personality and meta-learning in terms of neural transferable factors

2022 Fiscal Year Final Research Report

• Project/Area Number: 19H04180
• Research Category: Grant-in-Aid for Scientific Research (B)
• Allocation Type: Single-year Grants
• Section: 一般
• Review Section: Basic Section 61040:Soft computing-related
• Research Institution: Kyoto University
• Principal Investigator: Ishii Shin 京都大学, 情報学研究科, 教授 (90294280)
• Project Period (FY): 2019-04-01 – 2022-03-31
• Keywords: 脳型人工知能 / ブレイン・マシン・インターフェース / 模倣学習 / 強化学習 / ベイズ推定 / 意思決定

Outline of Final Research Achievements

We clarified fMRI-based neural bases involved in bottom-up and top-down attention during a still image observation task; this result has been published in (Fujimoto, et al., 2023). Extending this approach to examine prior factors of image observers, we successfully constructed a Bayesian image recognition model that could reproduce observers’ behaviors well. With a hierarchical decision-making task, we developed a hierarchical Bayesian inference model and also examined neural bases in hierarchical uncertainty resolution within decision making.
Moreover, we developed a new autonomous learning method that combines adversarial learning and reinforcement learning for central pattern generator, and applied it to autonomous locomotion acquisition tasks by a quadruped robot similar. We found the simulator could produce stable locomotion on a variety of terrain by this method.

Free Research Field

計算神経科学

Academic Significance and Societal Importance of the Research Achievements

覚醒脳におけるトップダウン注意の関与を実験的に調べた研究は少なく、本研究の成果は学術的意義がある。以下のように新聞報道（オンライン版）された。
https://www.nikkei.com/article/DGXZQOUC132QF0T11C22A0000000/
また、敵対事例学習に中枢パターン生成器強化学習を組み合わせた新しい学習法は、ロボットのみならず、自動車などの人工物の制御にも利用可能と期待される。この成果は制御系で最大級の国際会議IFAC 2023の招待セッションにて発表される。