Deceptive Planning for Resource Allocation

We consider a team of autonomous agents that navigate in an adversarial environment and aim to achieve a task by allocating their resources over a set of target locations. The adversaries in the environment observe the autonomous team's behavior to infer their objective and counter-allocate their own resources to the target locations. In this setting, we develop strategies for controlling the density of the autonomous team so that they can deceive the adversaries regarding their objective while achieving the desired final resource allocation. We first develop a prediction algorithm, based on the principle of maximum entropy, to express the team's behavior expected by the adversaries. Then, by measuring the deceptiveness via Kullback-Leibler divergence, we develop convex optimization-based planning algorithms that deceives adversaries by either exaggerating the behavior towards a decoy allocation strategy or creating ambiguity regarding the final allocation strategy. Finally, we illustrate the performance of the proposed algorithms through numerical simulations.