Identifying Sources and Sinks in the Presence of Multiple Agents with Gaussian Process Vector Calculus
This addresses the challenge of inferring causal features in multi-agent systems operating in diverse, non-stationary environments, representing a novel method for a known bottleneck.
The paper tackles the problem of identifying sources and sinks from agent trajectories in time-varying fields, presenting GP-LAPLACE which infers spatio-temporal vector fields and vector calculus operations using Gaussian processes without environment knowledge. It demonstrates applicability on synthetic and real-world GPS data with superiority over existing methods.
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.