In systems of multiple agents, identifying the cause of observed agent dynamics is challenging. Often, these agents operate in diverse, non-stationary environments, where models rely on hand-crafted environment-specific features to infer influential regions in the system's surroundings. To overcome the limitations of these inflexible models, we present GP-LAPLACE, a technique for locating sources and sinks from trajectories in time-varying fields. Using Gaussian processes, we jointly infer a spatio-temporal vector field, as well as canonical vector calculus operations on that field. Notably, we do this from only agent trajectories without requiring knowledge of the environment, and also obtain a metric for denoting the significance of inferred causal features in the environment by exploiting our probabilistic method. To evaluate our approach, we apply it to both synthetic and real-world GPS data, demonstrating the applicability of our technique in the presence of multiple agents, as well as its superiority over existing methods.

GP-LAPLACE是一种使用高斯过程从仅代理轨迹中推断出多智能体系统中的源和汇的技术，并利用概率方法在不需要知道环境的情况下确定推断因果特征在环境中的重要性，并且应用到合成和实际的GPS数据中，证明了其在多智能体存在的情况下的适用性，以及其优于现有方法的优越性。

高斯过程向量微积分识别多个智能体存在下的资源来源和汇点