Collective Intelligence in Living/non-living agents

池上 高志 教授

東京大学大学院 総合文化研究科


In this study, we propose a new theory of collective intelligence by using machine learning algorithms to study the dynamics of biological and non-living populations and their collective phenomena. The goal is to construct a new theory of emergent phenomena in population dynamics, including macroscopic emergent phenomena called superorganisms, by analyzing a large amount of individual tracking data obtained from actual biological experiments.  The ultimate goal is to discuss the relationship between macroscopic properties such as aging and vitality at the population level and the complex microscopic dynamics of the constituent individuals. Specifically, I will report on population phenomena in simulations of large scale swarms, as well as in biological populations such as honeybees, amphipods, and tetrahymena.

Takashi Ikegami, Yoh-ichi Mototake, Shintaro Kobori, Mizuki Oka, Yasuhiro Hashimoto: Life as an emergent phenomenon: studies from a large-scale boid simulation and web data, Phil.Roy.Soc.,375, pp.1-15, 2017.
Norihiro Maruyama, Daichi Saito, and Takashi Ikegami. Emergence of Superorganisms in a Large Scale Boids Model. 2019 IEEE Symposium Series on Computational Intelligence (SSCI), pp.299-305
Daichi Saito, Norihiro Maruyama, Takashi Ikegami and Yasuhiro Hashimoto. Visualization of Dynamic Structure in Flocking Behavior. Artificial Life and Robotics volume 25, pages544–551 (2020)


日時: 2021年12月21日(火) 16:00~17:30
場所: Zoom
連絡先: 理学系研究科 生物科学専攻 生物情報科学科
黒田 真也(skuroda AT bs.s.u-tokyo.ac.jp)

info.kuroda-lab [at] bs.s.u-tokyo.ac.jp