机器人与斑马鱼混合群体中鱼类集体行为神经网络模型的进化优化
Evolutionary optimisation of neural network models for fish collective behaviours in mixed groups of robots and zebrafish
摘要：
动物和机器人的社会互动对行为学研究和机器人学都很有趣。一方面，机器人可以成为分析动物集体行为的工具和模型，另一方面，机器人及其人工智能与自然动物集体智能直接面对和比较。第一步是设计能够与动物进行社交互动的机器人及其行为控制器。设计这种行为仿生控制器仍然是一个重要的挑战，因为它们必须再现动物的行为，并且必须根据实验数据进行校准。大多数动物集体行为模型是由建模者根据实验数据设计的。这一过程漫长而昂贵，因为很难确定相关的行为特征，然后将其作为建立模型的先验知识。在这里，我们想要模拟鱼的个体和集体行为，以开发机器人控制器。我们探讨使用优化的黑盒模型的基础上，人工神经网络（ANN）模拟鱼的行为。虽然人工神经网络可能不是仿生的，而是生物启发的，但它们可以用来将感知与运动反应联系起来。这些模型被设计为可实现的机器人控制器，以形成混合组的鱼和机器人，使用一些先验知识的鱼的行为。我们提出了一种方法，多层感知器或回声状态网络，通过进化算法进行优化，准确地模拟鱼的个人和集体行为在一个有界的矩形舞台。我们评估所生成的模型的仿生学，并将其与鱼类实验行为进行比较。
英文摘要：
Animal and robot social interactions are interesting both for ethological studies and robotics. On the one hand, the robots can be tools and models to analyse animal collective behaviours, on the other hand, the robots and their artificial intelligence are directly confronted and compared to the natural animal collective intelligence. The first step is to design robots and their behavioural controllers that are capable of socially interact with animals. Designing such behavioural bio-mimetic controllers remains an important challenge as they have to reproduce the animal behaviours and have to be calibrated on experimental data. Most animal collective behavioural models are designed by modellers based on experimental data. This process is long and costly because it is difficult to identify the relevant behavioural features that are then used as a priori knowledge in model building. Here, we want to model the fish individual and collective behaviours in order to develop robot controllers. We explore the use of optimised black-box models based on artificial neural networks (ANN) to model fish behaviours. While the ANN may not be biomimetic but rather bio-inspired, they can be used to link perception to motor responses. These models are designed to be implementable as robot controllers to form mixed-groups of fish and robots, using few a priori knowledge of the fish behaviours. We present a methodology with multilayer perceptron or echo state networks that are optimised through evolutionary algorithms to model accurately the fish individual and collective behaviours in a bounded rectangular arena. We assess the biomimetism of the generated models and compare them to the fish experimental behaviours.
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