| 研究課題/領域番号 |
23KF0108
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| 研究種目 |
特別研究員奨励費
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| 配分区分 | 基金 |
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| 応募区分 | 外国 |
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| 審査区分 |
小区分61040:ソフトコンピューティング関連
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| 研究機関 | 東京大学 |
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研究代表者 |
池上 高志 東京大学, 大学院総合文化研究科, 教授 (10211715)
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| 研究分担者 |
CROSSCOMBE MICHAEL 東京大学, 大学院総合文化研究科, 外国人特別研究員
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| 研究期間 (年度) |
2023-07-26 – 2026-03-31
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| 研究課題ステータス |
交付 (2023年度)
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| 配分額 *注記 |
2,100千円 (直接経費: 2,100千円)
2025年度: 600千円 (直接経費: 600千円)
2024年度: 700千円 (直接経費: 700千円)
2023年度: 800千円 (直接経費: 800千円)
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| キーワード | Collective intelligence / Collective dynamics / emergent behavior / neuroevolution / simulation |
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| 研究開始時の研究の概要 |
The main goals of this project are: 1) To study the impact of spatial constraints and information bottlenecks on collective behaviour, to improve our understanding of the necessary conditions for collective intelligence; 2) To develop a new framework for the evolution of collective behaviours which incorporates constraints observed in living systems; 3) To demonstrate the validity of this approach by evolving new collective behaviours in simulation.
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| 研究実績の概要 |
This year we have developed a simulation environment to facilitate the evolution of artificial neural networks (neuroevolution) to infer a general model of collective behaviour exhibited by a target living system’s observed dynamics. In this case, our simulation environment uses collective behaviour data obtained from recordings of real ant colony dynamics. This is then used to automatically evolve neural network topologies which attempt to reproduce the general behaviours observed in living systems. This framework can be applied to any living system whereby the observable dynamics can be represented as a time series of 2-dimensional positional data.
So far, we have submitted an extended abstract about the simulation environment which we intend to make available to the research community.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
At the ALIFE2023 conference (hosted by Prof. Iizuka at Hokkaido University), our paper on the results of the information bottleneck approach was accepted for an oral presentation. Our overall research plan is making good progress.
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| 今後の研究の推進方策 |
From here, we are already investigating methods to evolve more complex neural network topologies which are suitable for capturing the complex dynamics of living systems. First, we will redesign the current reward function to ensure that the general dynamics of the living system being modelled are accurately reproduced for learning. Then, extending existing algorithms for topological neuroevolution (NEAT, WANNs), we will evolve networks with more sophisticated internal architectures that might eventually evolve internal state representations. To do this, we will no longer stick to a strictly feed-forward architecture but instead allow the evolutionary process to use recurrent connections and additional activation functions in tandem. We expect this will produce more accurate dynamics.
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