用多臂老虎机模拟蜜蜂决策，能预测其觅食行为并解释个体学习策略。
Buzz, Choose, Forget: A Meta-Bandit Framework for Bee-Like Decision Making
- 基于带时间窗口的多臂老虎机，模拟蜜蜂有限记忆下的决策过程。
- 在真实、模拟及小鼠数据上表现优于基线模型，最优窗口为7次试验。
- 可解释个体学习差异，适用于生态学中的行为轨迹预测。
本文提出MAYA，一种基于多臂老虎机的序列模仿学习模型，用于再现和预测个体蜜蜂在情境化觅食任务中的决策行为。模型通过时间窗τ模拟蜜蜂的有限记忆，其最优值约为7次试验，且受天气条件轻微影响。在真实、模拟及互补（小鼠）数据集上的实验表明，MAYA（尤其是使用Wasserstein距离时）优于模仿学习基线和经典统计模型，同时提供个体学习策略的可解释性，并能推断出具有生态意义的未来行为轨迹。
原文摘要 · Abstract (English)
This work introduces MAYA, a sequential imitation learning model based on multi-armed bandits, designed to reproduce and predict individual bees' decisions in contextualized foraging tasks. The model accounts for bees' limited memory through a temporal window $τ$, whose optimal value is around 7 trials, with a slight dependence on weather conditions. Experimental results on real, simulated, and complementary (mice) datasets show that MAYA (particularly with the Wasserstein distance) outperforms imitation baselines and classical statistical models, while providing interpretability of individual learning strategies and enabling the inference of realistic trajectories for prospective ecological applications.
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