Persistent surveillance of events with unknown, time-varying statistics
We consider the problem of monitoring stochastic, time-varying events occurring at discrete locations. Our problem formulation extends prior work in persistent surveillance by considering the objective of maximizing event detections in unknown, dynamic environments where the rates of events are time-inhomogeneous and may be subject to abrupt changes. We propose a novel monitoring algorithm that effectively strikes a balance between exploration and exploitation as well as a balance between remembering and discarding information to handle temporal variations in unknown environments. We present an analysis proving the long-run average optimality of the policies generated by our algorithm under the assumption that the total temporal variations are sub-linear. We present simulation results demonstrating the effectiveness of our algorithm in several monitoring scenarios inspired by real-world applications, and its robustness to both continuous-random and abrupt changes in the statistics of the observed processes.
