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We build a model using Gaussian processes to infer a spatio-temporal vectorfield from observed agent trajectories. Significant landmarks or influencepoints in agent surroundings are jointly derived through vector calculusoperations that indicate presence of sources and sinks. We evaluate theseinfluence points by using the Kullback-Leibler divergence between the posteriorand prior Laplacian of the inferred spatio-temporal vector field. Throughlocating significant features that influence trajectories, our model aims togive greater insight into underlying causal utility functions that determineagent decision-making. A key feature of our model is that it infers a jointGaussian process over the observed trajectories, the time-varying vector fieldof utility and canonical vector calculus operators. We apply our model to bothsynthetic data and lion GPS data collected at the Bubye Valley Conservancy insouthern Zimbabwe.

Learning from lions: inferring the utility of agents from their trajectories