We propose Turing Learning, a novel system identification method for inferring the behavior of natural or artificial systems. Turing Learning simultaneously optimizes two populations of computer programs, one representing models of the behavior of the system under investigation, and the other representing classifiers. By observing the behavior of the system as well as the behaviors produced by the models, two sets of data samples are obtained. The classifiers are rewarded for discriminating between these two sets, that is, for correctly categorizing data samples as either genuine or counterfeit. Conversely, the models are rewarded for ‘tricking’ the classifiers into categorizing their data samples as genuine. Unlike other methods for system identification, Turing Learning does not require predefined metrics to quantify the difference between the system and its models. We present two case studies with swarms of simulated robots and prove that the underlying behaviors cannot be inferred by a metric-based system identification method. By contrast, Turing Learning infers the behaviors with high accuracy. It also produces a useful by-product—the classifiers—that can 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 individuals in the swarm, which may have significant implications for the study of animal collectives. Furthermore, Turing Learning could prove useful whenever a behavior is not easily characterizable using metrics, making it suitable for a wide range of applications.

中文翻译：

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图灵学习：一种无度量的行为推断方法及其在群体中的应用

我们提出了 图灵学习，一种用于推断自然系统或人工系统行为的新颖系统识别方法。图灵学习同时优化了两个计算机程序种群，一个代表被调查系统行为的模型，另一个代表分类器。通过观察系统的行为以及模型产生的行为，获得了两组数据样本。分类器因区分这两个集合而获得奖励，即，将数据样本正确分类为真实或假冒的。相反，这些模型因“诱使”分类器将其数据样本归类为真实数据而获得奖励。与其他系统识别方法不同，Turing Learning不需要预定义的指标即可量化系统及其模型之间的差异。我们目前使用大量模拟机器人进行案例研究，并证明无法通过基于度量的系统识别方法来推断基本行为。相比之下，图灵学习推断行为的准确性很高。它还产生了有用的副产品-分类器-可用于检测群体中的异常行为。此外，我们证明了图灵学习还可以成功地推断物理机器人群的行为。结果表明，可以从群体中个体的运动轨迹直接推断出集体行为，这可能对动物集体的研究具有重要意义。此外，只要不容易使用指标来表征行为，Turing Learning就会证明是有用的，从而使其适用于广泛的应用程序。