Extracting the heterogeneous strategies from crowd data using evolutionary computation

Abstract

Crowd simulation is one of the most widely used technique for the design and evaluation of the human-in-the-loop situation such as evacuation plans, the building designs and so on, in a virtual environment. In order to have a valid evaluation, it is necessary to have a correct model of an individual agent’s decision process which causes a behavior of human’s in a crowd. However, in general, designing a decision process of agent’s largely depends on a trial-and-error manner. To avoid the trial-and-error by human designers, we focus on the automated method to derive agent’s decision strategy from the real data of human’s. In this paper, we consider a decision process consists of two stages. One is the strategy phase to select a goal state, and the other is the policy phase to output the primitive action of agent’s. We focus on the strategy phase. Though it should be more natural to assume that the strategy of each agent is not all the same, the existing method assumes that all agents have a common and homogeneous strategy. The proposed method makes it possible to extract the individual and different strategies of agent’s by evolutionary computation. The results of the experiments show the validity of our method. In addition, it is shown that there exist the cases where multiple strategies will be extracted for a single trajectory.