Emergent effects in massive agent swarms in real-time game environments

Computational efficiency and hence the scale of agent-based swarm simulations is bound by the nearest neighbour computation for each agent. This article proposes the use of GPU texture memory to implement lookup tables for a spatial partitioning based k-Nearest Neighbours algorithm. These improvements allow simulation of swarms of 2
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agents at higher rates than the current best alternative algorithms. This approach is incorporated into an existing framework for simulating steering behaviours allowing for a complete implementation of massive agent swarm simulations, with per agent behaviour preferences, on a Graphics Processing Unit. These simulations have enabled an investigation of the emergent dynamics that occur when massive swarms interact with a choke point in their environment. Various modes of sustained dynamics with temporal and spatial coherence are identified when a critical mass of agents is simulated and some elementary properties are presented. The algorithms presented in this article enable researchers and content designers in games and movies to implement truly massive agent swarms in real time and thus provide a basis for further identification and analysis of the emergent dynamics in these swarms. This will improve not only the scale of swarms used in commercial games and movies but will also improve the reliability of swarm behaviour with respect to content design goals.