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.

翻译：考虑一个由自主智能体组成的团队，该团队在对抗性环境中导航，并通过在多个目标位置分配资源来完成任务。环境中的对手会观察自主团队的行为以推断其目标，并采取对抗措施。在此背景下，我们提出控制自主团队密度的策略，使其既能欺骗对手关于自身目标的判断，同时实现期望的最终资源分配。首先，基于最大熵原理开发一种预测算法，用于表达对手预期的团队行为。随后，通过使用库尔贝克-莱布勒散度衡量欺骗性，设计基于凸优化的规划算法，通过夸大团队向诱饵分配策略的行为或制造最终分配策略的模糊性来欺骗对手。一项包含320名参与者的用户研究表明，所提出的算法能有效实现欺骗，并揭示了参与者对近期目标的固有偏好。