Learning From Humans: Agent Modeling With Individual Human Behaviors

Multiagent-based simulation (MABS) is a very active interdisciplinary area bridging multiagent research and social science. The key technology to conduct truly useful MABS is agent modeling for reproducing realistic behaviors. In order to make agent models realistic, it seems natural to learn from human behavior in the real world. The challenge presented in this paper is to obtain an individual behavior model by using participatory modeling in the traffic domain. We show a methodology that can elicit prior knowledge for explaining human driving behavior in specific environments, and then construct a driving behavior model based on the set of prior knowledge. In the real world, human drivers often perform unintentional actions, and occasionally, they have no logical reason for their actions. In these cases, we cannot rely on prior knowledge to explain them. We are forced to construct a behavior model with an insufficient amount of knowledge to reproduce the driving behavior. To construct such individual driving behavior model, we take the approach of using knowledge from others to complement the lack of knowledge from the target. To clarify that the behavior model including prior knowledge from others offers individuality in driving behavior, we experimentally confirm that the driving behaviors reproduced by the hybrid model correlate reasonably well with human behavior.