自動的な知識の抽出に基づく動物行動学に基づくソーシャルロボットの行動モデルの獲得

在此期间，研究了在基于模糊规则内插的强化学习(FRI-RL)中使用多个代理并将它们分布式并行运行的可能性。由于Fri-RL知识提取方法本质上是顺序的，因此在并行版本中一些子结果可能不同，但仍然提供了足够的解决方案。这种方法可以更快地进行知识提取，因此将该方法用于高维问题变得实用。此外，还部分设计并实现了行为模拟模型(用基于Fri的模糊自动机实现的奇异情况测试(SST))与真实物理机器人之间的接口。用真实的实体机器人进行实验正在进行中。此外，构建了一个合适的室内定位系统，并使其适应计划中的人-机器人交互(HRI)场景的需要。该系统能够很容易地将室内定位系统的虚拟坐标系校准到真实世界的物理坐标系，这使得我们计划的利用真人和移动机器人进行HRI实验成为可能。开发了一个定制的机器人行为引擎和一个运动控制系统，以支持拟议的人工奇异情况测试实验。

In this period the possibility of using multiple agents in the Fuzzy Rule Interpolation-based Reinforcement Learning (FRI-RL) and running them distributed in parallel was investigated. As the FRI-RL knowledge extraction method is inherently sequential, some sub-results can be different in the parallel version, but still providing a sufficient solution. This way the knowledge extraction can be performed much faster, therefore using the method on problems with a high dimension count becomes practical. Also, a possible bridging interface between the behaviour simulation model (Strange Situation Test (SST) realized with an FRI-based fuzzy automaton) and real physical robots have been partly designed and implemented. Experimenting with real physical robots is underway. Furthermore a suitable indoor localization system was constructed and adapted to the needs of the planned Human-Robot Interaction (HRI) scenario. This system is able to easily calibrate the indoor localization system’s virtual coordinate system to the real-world physical coordinate system, which makes our planned HRI experiments possible with real humans and mobile robots. A customized robot behaviour engine and a motion control system was developed to support the proposed artificial Strange Situation Test experiments.