事件的世界：关于因果关系的部分知识的演绎
Worlds of Events: Deduction with Partial Knowledge about Causality
摘要：
互联网用户之间的交互是通过他们的设备和数据中心的公共支持基础设施来进行的。跟踪在这个分布式系统中发生的动作之间的因果关系是提供一个无缝交互的关键，其中效果跟随原因。由于保持大量元数据的成本，在大规模交互中跟踪因果关系是困难的;当处理资源有限的设备时更具挑战性。在本文中，我们专注于保持部分知识的因果关系和地址演绎从知识。我们提供了第一个证明理论的因果关系模型的分布部分知识。我们证明可计算性和一致性的结果。我们还证明了部分知识产生一个弱模型比经典的因果关系。我们为因果关系的离线推理提供了规则，并反驳了一些相关的民间传说。我们定义了两个概念的设备之间的向前和向后的双相似性，使用它，我们证明了两个重要的结果。也就是说，无论加法/减法的顺序如何，只要：（1）相同的因果信息被馈送给两个设备，两个设备就可以相似地推导出因果关系。(2)它们开始双相似并删除相同的因果信息。由于我们分别建立了向前和向后的双相似性，后两个结果的证明可以通过简单的长度归纳来实现。
英文摘要：
Interactions between internet users are mediated by their devices and the common support infrastructure in data centres. Keeping track of causality amongst actions that take place in this distributed system is key to provide a seamless interaction where effects follow causes. Tracking causality in large scale interactions is difficult due to the cost of keeping large quantities of metadata; even more challenging when dealing with resource-limited devices. In this paper, we focus on keeping partial knowledge on causality and address deduction from that knowledge. We provide the first proof-theoretic causality modelling for distributed partial knowledge. We prove computability and consistency results. We also prove that the partial knowledge gives rise to a weaker model than classical causality. We provide rules for offline deduction about causality and refute some related folklore. We define two notions of forward and backward bisimilarity between devices, using which we prove two important results. Namely, no matter the order of addition/removal, two devices deduce similarly about causality so long as: (1) the same causal information is fed to both. (2) they start bisimilar and erase the same causal information. Thanks to our establishment of forward and backward bisimilarity, respectively, proofs of the latter two results work by simple induction on length.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
