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，一种基于多臂赌博机的序列模仿学习模型，旨在复现并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$τ$来考虑蜜蜂的有限记忆能力，其最优值约为7次尝试，且略微依赖于天气条件。在真实、模拟以及补充（小鼠）数据集上的实验结果表明，MAYA（特别是采用Wasserstein距离时）在模仿基准和经典统计模型上表现更优，同时能够提供个体学习策略的可解释性，并可为前瞻性生态应用推断出真实的轨迹。