Usage of automated controllers which make decisions on an environment are widespread and are often based on black-box models. We use Knowledge Compilation theory to bring explainability to the controller's decision given the state of the system. For this, we use simulated historical state-action data as input and build a compact and structured representation which relates states with actions. We implement this method in a Traffic Light Control scenario where the controller selects the light cycle by observing the presence (or absence) of vehicles in different regions of the incoming roads.

中文翻译：

---

使用知识汇编的智能交通系统的可解释性：交通灯控制器案例

对环境做出决策的自动化控制器的使用很普遍，并且通常基于黑盒模型。在给定系统状态的情况下，我们使用知识编译理论为控制器的决策带来可解释性。为此，我们使用模拟的历史状态-动作数据作为输入，并构建了一个紧凑的结构化表示，将状态与动作联系起来。我们在交通灯控制场景中实现此方法，其中控制器通过观察进入道路不同区域中车辆的存在（或不存在）来选择光周期。