We propose Turing Learning, a novel system identification method for inferring behavior. Turing Learning simultaneously optimizes models and classifiers. The classifiers are provided with data samples from both an agent and models under observation, and are rewarded for discriminating between them. Conversely, the models are rewarded for 'tricking' the classifiers into categorizing them as the agent. Unlike other methods for system identification, Turing Learning does not require predefined metrics to quantify the difference between the agent and models. We present two case studies with swarms of simulated robots that show that Turing Learning outperforms a metric-based system identification method in terms of model accuracy. The classifiers perform well collectively and could be used to detect abnormal behavior in the swarm. Moreover, we show that Turing Learning also successfully infers the behavior of physical robot swarms. The results show that collective behaviors can be directly inferred from motion trajectories of a single agent in the swarm, which may have significant implications for the study of animal collectives.

通过Turing Learning可以同时对自然或人工系统进行建模并进行行为推断，可以更准确地推断系统的行为, 并适用于模型已知的系统以及模型未知的系统。

Turing学习: 一种基于度量无关的推断行为方法及其在群体中的应用