Adversarial Decision Making Against Intelligent Targets in Cooperative Multiagent Systems

In this article, we propose a distributed adversarial decision-making approach for multiagent system (MAS), which is supposed to detect a team of intelligent targets. The MAS is demanded to achieve the best detection benefit against the intelligent targets that can shelter part of their features and prevent the detection according to an expert defense policy. One of the key challenges is how to achieve higher detection benefit that is both determined by the target assignment action of the MAS and the nonpredictable defense policy of the targets. To handle this problem, we first formulate the multiagent decision-making problem as a max–min problem of detection benefit and break it into separate parts that are easier to be optimized. Then, we introduce a new variant of distributed alternating direction method of multipliers (ADMM) to search the optimal solutions under the worst defense policy that the targets choose. To overcome the lack of access to the global convergence of multiblock ADMM, we add local additional variables to formulate a penalty for nonconvex parts of the local objective function. The convergence to an equilibrium and the optimality of the detection benefit are empirically validated by numerical simulations. The influence of the parameter setting is also presented and can be regarded as a prior suggestion for real applications.