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.

翻译：本文提出了MAYA，一个基于多臂赌博机的序列模仿学习模型，旨在重现和预测个体蜜蜂在情境化觅食任务中的决策。该模型通过时间窗口τ来考虑蜜蜂有限的记忆能力，其最优值约为7次试验，且受天气条件轻微影响。在真实、模拟及补充（小鼠）数据集上的实验结果表明，MAYA（特别是结合Wasserstein距离时）优于模仿学习基线模型和经典统计模型，同时能够解读个体学习策略，并支持为前瞻性生态应用推断逼真的运动轨迹。