动物和机器人混合社会中集体行为的多级模型的自动优化
Automated optimisation of multi-level models of collective behaviour in a mixed society of animals and robots
摘要：
动物和机器人的集体行为可以表现出复杂的动态，需要多层次的描述。在这里，我们有兴趣开发一个多层次的建模框架，使用机器人在动物集体决策的研究。在这种情况下，使用机器人可以用于在计算机上验证模型，诱导校准的重复刺激以触发动物反应或调节和控制动物的集体行为。然而，设计适当的仿生机器人行为面临着一个重大挑战：如何从动物观察到的集体决策动态到机器人的实际算法实现。在以前的工作中，这主要是手工完成的，通常是从人类设计的模型中获得灵感。通常，行为模型是宏观的，种群动态的微分方程，或微观的，每个个体的明确时空状态。只有微观模型可以很容易地实现为机器人控制器。在这里，我们解决的问题，可以在机器人中实现的较低级别的描述模型的设计自动化，并表现出相同的集体动态作为一个给定的更高级别的模型。我们采用进化算法，同时优化模型的参数占不同层次的描述。这种方法适用于实验验证的庇护所选择问题解决群居昆虫和机器人。我们成功地设计和自动校准微观和混合模型表现出相同的动态作为一个宏观的。我们的框架可用于动物或机器人群体和生物混合系统的集体行为的多层次建模。
英文摘要：
Animal and robotic collective behaviours can exhibit complex dynamics that require multi-level descriptions. Here, we are interested in developing a multi-level modeling framework for the use of robots in studies about animal collective decision-making. In this context, using robots can be useful for validating models in silico, inducing calibrated repetitive stimuli to trigger animal responses or modulating and controlling animal collective behaviour. However, designing appropriate biomimetic robotic behaviour faces a major challenge: how to go from the collective decision dynamics observed with animals to an actual algorithmic implementation in robots. In previous work, this was mainly done by hand, often by taking inspiration from human-designed models. Typically, models of behaviour are either macroscopic, differential equations of the population dynamics, or microscopic,explicit spatio-temporal state of each individual. Only microscopic models can easily be implemented as robot controllers. Here, we address the problem of automating the design of lower level description models that can be implemented in robots and exhibit the same collective dynamics as a given higher level model. We apply evolutionary algorithms to simultaneously optimise the parameters of models accounting for different levels of description. This methodology is applied to an experimentally validated shelter-selection problem solved by gregarious insects and robots. We successfully design and calibrate automatically both a microscopic and a hybrid model exhibiting the same dynamics as a macroscopic one. Our framework can be used for multi-level modeling of collective behaviour in animal or robot populations and bio-hybrid systems.
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