Multi-agent multi-target search problems, where the targets are capable of movement, require sophisticated algorithms for near-optimal performance. While there are several algorithms for agent control, comparatively less attention has been paid to near-optimal target behaviours. Here, a state-of-the-art algorithm for targets to avoid a single agent called TrailMax has been adapted to work within a multiple agents and multiple targets framework. The aim of the presented algorithm is to make the targets avoid capture as long as possible, if possible until timeout. Empirical analysis is performed on grid-based gaming benchmarks. The results suggest that Multiple Pursuers TrailMax reduces the agent success rate by up to 15% as compared to several previously used target control algorithms and increases the time until capture in successful runs.
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Multiple Pursuers TrailMax Algorithm for Dynamic Environments
Semantic Scholar · Computer Science · 2021
Abstract
Multi-agent multi-target search problems, where the targets are capable of movement, require sophisticated algorithms for near-optimal performance. While there are several algorithms for agent control, comparatively less attention has been paid to near-optimal target behaviours. Here, a state-of-the-art algorithm for targets to avoid a single agent called TrailMax has been adapted to work within a multiple agents and multiple targets framework. The aim of the presented algorithm is to make the targets avoid capture as long as possible, if possible until timeout. Empirical analysis is performed on grid-based gaming benchmarks. The results suggest that Multiple Pursuers TrailMax reduces the agent success rate by up to 15% as compared to several previously used target control algorithms and increases the time until capture in successful runs.