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Turing learning: a metric-free approach to inferring behavior and its application to swarms

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. It also produces a useful byproduct in the form of 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 individual agents in the swarm, which may have significant implications for the study of animal collectives. Furthermore, Turing Learning could prove useful wherever a behavior is not easily characterizable using metrics, making it suitable for a wide range of applications.”

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