发表机构
Sorbonne University Abu Dhabi; BioRobotics Institute, Sant’Anna School of Advanced Studies; Division of Computing and Mathematical Science, MBZUAI（阿布扎比索邦大学; 圣安娜高等学院机器人研究所; MBZUAI计算与数学科学系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对经典鱼群集群行为模型对参数高度敏感，本研究通过生物混合实验验证，提出加入探索项的新模型，兼具鲁棒性且能复现集群与探索行为，为集群行为建模需兼顾社会互动与个体探索。
AI 中文摘要
鱼群的集群行为通常基于如下假设建模：每个个体遵循取决于自身相对于邻居的位置和速度的特定运动规则。尽管这些模型复现了自然界中观察到的许多鱼群模式，但尚不清楚所假设的规则在个体层面是否符合实际。为解决这一问题，我们首先分析了一组实验：一条活鱼与水箱中四条移动的机器鱼互动，并测量它在相对于机器鱼各个位置停留的时间。随后我们模拟了该实验，用智能体替换活鱼，评估经典模型与实验观测结果的吻合程度。对参数空间的广泛探索表明，这些模型在选择非常特定的参数时，能紧密匹配实验观测结果。但它们对参数值高度敏感：微小扰动就会导致模型完全失效。为解决该问题，我们提出一种新模型，加入额外的探索项来表征智能体每一时刻的位置偏好。该模型被证明对参数的微小误差具有鲁棒性，同时能成功复现实验结果。此外，当推广到多条鱼的场景时，该模型复现了集群行为和常见的鱼群模式，同时在个体和群体层面都表现出探索行为。这表明社会凝聚力与个体探索行为共存，符合实际的集群行为模型应同时考虑社会互动和这一探索成分。
英文摘要
Collective behavior of fish schools is usually modeled on the assumption that each individual follows specific rules of motion that depend on its position and velocity relative to its neighbors. Although these models reproduce many schooling patterns observed in nature, it remains unclear whether the assumed rules are realistic at the individual level. To address this question, we first analyzed a set of experiments in which a live fish interacted with four moving robotic fish in a tank, and measured the time it spent in each position relative to the robots. We then simulated this experiment, replacing the fish with an agent, and assessed the extent to which classical models agree with the experimental observations. An extensive exploration of the parameter space showed that these models closely matched the experimental observations, with a very specific choice of parameters. However, they were highly sensitive to the parameter values: a minor perturbation caused them to fail completely. To resolve this, we propose a new model that incorporates an additional exploratory term characterizing the agent's positional preference at every moment. This model proved robust to small errors in the parameters while successfully replicating the experiments. Furthermore, when generalised to multiple fish, the model reproduced schooling behaviour and common schooling patterns, while also exhibiting exploratory behaviour at both the individual and the school level. This shows that social cohesion coexists with individual exploratory behaviour, and that realistic models of collective behaviour should account for both social interactions and this exploratory component.
Comments38 pages, 8 figures, 2 tables; supplementary material with 2 figures and 3 tables
