Smart homes are Cyber-Physical Systems (CPS) where multiple devices and controllers cooperate to achieve high-level goals. Causal knowledge on relations between system entities is essential for enabling system self-adaption to dynamic changes. As house configurations are diverse, this knowledge is difficult to obtain. In previous work, we proposed to generate Causal Bayesian Networks (CBN) as follows. Starting with considering all possible relations, we progressively discarded non-correlated variables. Next, we identified causal relations from the remaining correlations by employing “do-operations.” The obtained CBN could then be employed for causal inference. The main challenges of this approach included “non-doable variables” and limited scalability. To address these issues, we propose three extensions: (i) early pruning weakly correlated relations to reduce the number of required do-operations, (ii) introducing aggregate variables that summarize relations between weakly coupled sub-systems, and (iii) applying the method a second time to perform indirect do interventions and handle non-doable relations. We illustrate and evaluate the efficiency of these contributions via examples from the smart home and power grid domain. Our proposal leads to a decrease in the number of operations required to learn the CBN and in an increased accuracy of the learned CBN, paving the way toward applications in large CPS.

智能家居是网络物理系统 (CPS)，其中多个设备和控制器相互协作以实现高级目标。关于系统实体之间关系的因果知识对于使系统能够自适应动态变化至关重要。由于房屋结构多种多样，这方面的知识很难获得。在之前的工作中，我们建议按如下方式生成因果贝叶斯网络（CBN）。从考虑所有可能的关系开始，我们逐渐丢弃不相关的变量。接下来，我们通过使用“ do-operations”从剩余的相关性中识别因果关系”。获得的 CBN 可以用于因果推理。这种方法的主要挑战包括“不可行的变量”和有限的可扩展性。为了解决这些问题，我们提出了三个扩展：（i）早期修剪弱相关关系以减少所需的操作数量，（ii）引入总结弱耦合子系统之间关系的聚合变量，以及（iii）应用方法第二次执行间接 do干预并处理不可行的关系。我们通过智能家居和电网领域的示例来说明和评估这些贡献的效率。我们的建议减少了学习 CBN 所需的操作数量，并提高了学习到的 CBN 的准确性，为大型 CPS 的应用铺平了道路。