  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. An adversary in the environment observes the autonomous
team's behavior to infer their objective and responds against the team. In this
setting, we propose strategies for controlling the density of the autonomous
team so that they can deceive the adversary 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 adversary. Then, by measuring the deceptiveness via
Kullback-Leibler divergence, we devise convex optimization-based planning
algorithms that deceive the adversary by either exaggerating the behavior
towards a decoy allocation strategy or creating ambiguity regarding the final
allocation strategy. A user study with $320$ participants demonstrates that the
proposed algorithms are effective for deception and reveal the inherent biases
of participants towards proximate goals.
