Multi‐agent search for a moving and camouflaging target
研究了多个异构智能体搜索随机移动和伪装目标的规划问题，提出了线性化、预处理和切割平面等算法，并通过数值实验比较了不同方法的效率。
Abstract In multi‐agent search planning for a randomly moving and camouflaging target, we examine heterogeneous searchers that differ in terms of their endurance level, travel speed, and detection ability. This leads to a convex mixed‐integer nonlinear program, which we reformulate using three linearization techniques. We develop preprocessing steps, outer approximations via lazy constraints, and bundle‐based cutting plane methods to address large‐scale instances. Further specializations emerge when the target moves according to a Markov chain. We carry out an extensive numerical study to show the computational efficiency of our methods and to derive insights regarding which approach should be favored for which type of problem instance.