Reactive Multi-Context Systems：动态环境下的异构推理
Reactive Multi-Context Systems: Heterogeneous Reasoning in Dynamic Environments
摘要：
管理多上下文系统（mMCSs）允许以模块化和非常通用的方式集成异构知识源。然而，它们主要是为静态场景而设计的，因此不太适合动态环境，在动态环境中，需要对不断到达的数据流进行连续推理。在本文中，我们介绍了反应式多上下文系统（rMCSs），反应式推理框架中存在的异构知识源和数据流。我们表明，rMCSs确实非常适合于这一目的，说明如何在流推理的上下文中出现的几个典型问题可以使用它们来处理，通过显示如何不一致可能发生在多个知识源的集成可以处理，并认为，潜在的非确定性rMCSs可以避免，如果需要使用替代，更怀疑的有根据的语义，而不是有益的计算属性。我们还研究了与rMCSs相关的各种推理问题的计算复杂性。最后，我们讨论了相关的工作，并表明，rMCSs不仅概括mMCSs的动态设置，但也捕获/扩展相关的方法w.r.t.动态知识表示和流推理。
英文摘要：
Managed multi-context systems (mMCSs) allow for the integration of heterogeneous knowledge sources in a modular and very general way. They were, however, mainly designed for static scenarios and are therefore not well-suited for dynamic environments in which continuous reasoning over such heterogeneous knowledge with constantly arriving streams of data is necessary. In this paper, we introduce reactive multi-context systems (rMCSs), a framework for reactive reasoning in the presence of heterogeneous knowledge sources and data streams. We show that rMCSs are indeed well-suited for this purpose by illustrating how several typical problems arising in the context of stream reasoning can be handled using them, by showing how inconsistencies possibly occurring in the integration of multiple knowledge sources can be handled, and by arguing that the potential non-determinism of rMCSs can be avoided if needed using an alternative, more skeptical well-founded semantics instead with beneficial computational properties. We also investigate the computational complexity of various reasoning problems related to rMCSs. Finally, we discuss related work, and show that rMCSs do not only generalize mMCSs to dynamic settings, but also capture/extend relevant approaches w.r.t. dynamics in knowledge representation and stream reasoning.
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。
