Evolution of Proxy Use in Neural Network Controllers for Crowd Modeling

How individuals' movement decisions lead to complex, large-scale behavior of crowds is a central subject in crowd modeling and simulation. Understanding this multiscale, emergent phenomenon can provide deeper insights into social dynamics in a crowd, and can help design real world applications such as evacuation planning systems. Crowd simulation is generally computationally intensive, especially when complex motion planning constraints are imposed on individual actors. Furthermore, altering such constraints requires major changes in the simulation engine. An effective way to deal with these issues is the use of intangible social factors (proxies). Proxies are agent-like entities that are dynamically generated and destroyed. They can be combined with a generic, domain-independent motion planner for computationally efficient simulation. These proxies are generally generated and destroyed based on a fixed set of rules, mimicking intangible social factors among individuals. In this paper, we propose the use of evolving neural networks (Neuroevolution of Augmenting Topologies, NEAT) to dynamically control the use of proxies. Our results show that the neural networks evolve to dynamically utilize the proxies, and proxy use increases performance in a simple evacuation task. These results suggest that proxy rules can be learned based on loosely defined fitness goals. We expect our method to be applicable to more complex behavioral modeling and simulation domains, beyond crowd modeling.