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

研究了在基于模糊规则插值的强化学习中使用多个智能体并并行分布运行的可能性。由于FRI-RL知识提取方法本质上是顺序的，在并行版本中，一些子结果可能会有所不同，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数问题是可行的。此外，行为模拟模型（用基于人工智能的模糊自动机实现的奇怪情况测试（SST））和真实物理机器人之间可能的桥接接口已经部分设计和实现。真实的物理机器人实验正在进行中。在此基础上，构建了适合人机交互场景的室内定位系统。该系统能够轻松地将室内定位系统的虚拟坐标系统校准为现实世界的物理坐标系统，这使得我们计划中的人类和移动机器人的HRI实验成为可能。开发了一个定制的机器人行为引擎和运动控制系统，以支持所提出的人工奇怪情况测试实验。

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.