从运动数据的时间序列推断跟随策略的框架
Framework for Inferring Following Strategies from Time Series of Movement Data
摘要：
群体如何在运动决策中达成共识？人们是否会追随他们的朋友、一个预先确定的领导者，或者任何碰巧在附近的人？为了解决这些问题的计算，我们形式化的“协调策略推理问题”。在这种情况下，一组多个人以协调的方式朝着目标路径移动。每个人都使用特定的策略来跟随其他人（例如最近的邻居，预定义的领导者，首选的朋友）。给定一组时间序列，其中包括协调的运动和一组候选策略作为输入，我们提供了第一种方法（尽我们所知）来推断每个人是否使用本地协议系统或类似的策略，以实现运动协调组的水平。我们评估和证明所提出的框架的性能，通过预测一组中的一个人在模拟数据集以及两个真实世界的数据集：一个学校的鱼和一群狒狒的运动方向。此外，由于没有事先的方法来推断个人层面的战略，我们比较我们的框架与国家的最先进的方法的任务分类的组级协调模型。结果表明，我们的方法是高度准确地推断出正确的策略，在模拟数据集，即使在复杂的混合策略设置，现有的方法可以推断。在组级协调模型的分类任务中，我们的框架在所有数据集中的表现都优于最先进的方法。动物数据实验表明，鱼类，正如预期的那样，遵循他们的邻居，而狒狒有一个偏好，以遵循特定的个人。我们的方法推广到任意的时间序列数据的实数，超越运动数据。
英文摘要：
How do groups of individuals achieve consensus in movement decisions? Do individuals follow their friends, the one predetermined leader, or whomever just happens to be nearby? To address these questions computationally, we formalize "Coordination Strategy Inference Problem". In this setting, a group of multiple individuals moves in a coordinated manner towards a target path. Each individual uses a specific strategy to follow others (e.g. nearest neighbors, pre-defined leaders, preferred friends). Given a set of time series that includes coordinated movement and a set of candidate strategies as inputs, we provide the first methodology (to the best of our knowledge) to infer whether each individual uses local-agreement-system or dictatorship-like strategy to achieve movement coordination at the group level. We evaluate and demonstrate the performance of the proposed framework by predicting the direction of movement of an individual in a group in both simulated datasets as well as two real-world datasets: a school of fish and a troop of baboons. Moreover, since there is no prior methodology for inferring individual-level strategies, we compare our framework with the state-of-the-art approach for the task of classification of group-level-coordination models. The results show that our approach is highly accurate in inferring the correct strategy in simulated datasets even in complicated mixed strategy settings, which no existing method can infer. In the task of classification of group-level-coordination models, our framework performs better than the state-of-the-art approach in all datasets. Animal data experiments show that fish, as expected, follow their neighbors, while baboons have a preference to follow specific individuals. Our methodology generalizes to arbitrary time series data of real numbers, beyond movement data.
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