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.

本文介绍了一种名为 “反应性多语境系统” 的框架，它能够支持异构知识源和数据流的连续推理，解决了异构知识可能导致的一致性问题，并允许使用优化的计算语义以避免潜在的非确定性。同时考察了与 rMCSs 相关的各种推理问题的计算复杂度，并探讨了其与知识表示和流推理中的动态性相关的方法扩展性和一般化。

响应式多上下文系统：动态环境中的异构推理

Reactive Multi-Context Systems: Heterogeneous Reasoning in Dynamic  Environments

We show in this paper how managed multi-context systems (mMCSs) can be turned
into a reactive formalism suitable for continuous reasoning in dynamic
environments. We extend mMCSs with (abstract) sensors and define the notion of
a run of the extended systems. We then show how typical problems arising in
online reasoning can be addressed: handling potentially inconsistent sensor
input, modeling intelligent forms of forgetting, selective integration of
knowledge, and controlling the reasoning effort spent by contexts, like setting
contexts to an idle mode. We also investigate the complexity of some important
related decision problems and discuss different design choices which are given
to the knowledge engineer.

本文介绍了如何将管理的多上下文系统转化为连续推理适用于动态环境的反应式形式，并讨论了在在线推理中可能出现的问题，如处理传感器输入、选择性集成知识以及控制上下文的推理努力等，并探讨了一些与此相关的决策问题的复杂度和可选设计方案。