A Strategy-Based Algorithm for Moving Targets in an Environment with Multiple Agents

Abstract

Most studies in the field of search algorithms have only focused on pursuing agents, while comparatively less attention has been paid to target algorithms that employ strategies to evade multiple pursuing agents. In this study, a state-of-the-art target algorithm, TrailMax, has been enhanced and implemented for multiple agent pathfinding problems. The presented algorithm aims to maximise the capture time if possible until timeout. Empirical analysis is performed on grid-based gaming benchmarks, measuring the capture cost, the success of escape and statistically analysing the results. The new algorithm, Multiple Pursuers TrailMax, doubles the escaping time steps until capture when compared with existing target algorithms and increases the target’s escaping success by 13% and in some individual cases by 37%.