Worlds of Events: Deduction with Partial Knowledge about Causality
Seyed Hossein Haeri (Universite catholique de Louvain, Belgium), Peter, Van Roy (Universite catholique de Louvain, Belgium), Carlos Baquero, (Universidade do Minho, Portugal), Christopher Meiklejohn (Universite, catholique de Louvain, Belgium)

TL;DR
This paper introduces a proof-theoretic model for reasoning about causality with partial knowledge in distributed systems, proving key properties and establishing bisimilarity concepts for consistent causal deduction across devices.
Contribution
It presents the first formal causality model for distributed partial knowledge, with proofs of computability, consistency, and bisimilarity-based deduction rules.
Findings
Partial knowledge leads to a weaker causality model than classical.
Devices deduce causality similarly if fed with the same information and starting bisimilar.
Proofs of bisimilarity results are simplified by induction on sequence length.
Abstract
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…
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Taxonomy
TopicsSemantic Web and Ontologies · Biomedical Text Mining and Ontologies · Topic Modeling
