Estimating Human Strategies by Collision Avoidance Experiments with Agents

Abstract

In human's cooperative behavior, there are some strategies: a passive behavioral strategy based on others’behaviors and an active behavioral strategy based on the objective-first. However, it is not clear how to acquire a meta-strategy to switch those strategies. In this study, we conduct a collision avoidance experiment with agents taking multiple strategies in a grid-like corridor to see whether subject's behavior changes when agent's strategy changes. We compare the behavior selected by the subjects with the behavior of the agents acquired by reinforcement learning. The experimental results show that subjects can read the change in strategy from the behavior of the oncoming agent.