Data-driven sequential goal selection model for multi-agent simulation

Abstract

With recent advances in distributed virtual worlds, online users have access to larger and more immersive virtual environments. Sometimes the number of users in virtual worlds is not large enough to make the virtual world realistic. In our paper, we present a crowd simulation algorithm that allows a large number of virtual agents to navigate around the virtual world autonomously by sequentially selecting the goals. Our approach is based on our sequential goal selection model (SGS) which can learn goal-selection patterns from synthetic sequences. We demonstrate our algorithm's simulation results in complex scenarios containing more than 20 goals.