text: Abstract 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.
  @cite_38: Among the challenges faced by the area of knowledge representation KR are the following ones: firstly, knowledge represented in different knowledge representation languages needs to be integrated, and secondly, certain applications have specific needs not typically fulfilled by standard KR systems. What we have in mind here are applications where reasoners, rather than being called by the user in order to answer some specific query, run online and have to deal with a continuous stream of information. In this paper we argue that multi-context systems MCS are adequate tools for both challenges. The original MCS approach was introduced to handle the integration problem in a principled way. A later extension to so-called managed MCS appears to provide relevant functionality for the second challenge. In this paper we review both MCS and managed MCS and discuss how the latter approach needs to be further developed for online applications.
  @cite_21: We introduce the first approach to Reactive Answer Set Programming, aiming at reasoning about real-time dynamic systems running online in changing environments. We start by laying the theoretical foundations by appeal to module theory. With this, we elaborate upon the composition of the various offline and online programs in order to pave the way for stream-driven grounding and solving. Finally, we describe the implementation of a reactive ASP solver, oclingo.
summary: Closely related to EVOLP are the two frameworks of Reactive ASP, one implemented as a solver @cite_21 and one described in @cite_38 . The system extends an ASP solver for handling external modules provided at runtime by a controller. The output of these external modules can be seen as the observations of EVOLP. Unlike the observations in EVOLP, which can be rules, external modules in are restricted to produce atoms so the evolving capabilities are very restricted. On the other hand, permits committing to a specific answer set at each state for the sake of efficiency, a feature that is not part of EVOLP. Reactive ASP as described in @cite_38 can be seen as a more straightforward generalization of EVOLP where operations other than @math for self-updating a program are permitted. Since EVOLP can be captured by , and since permit several (evolution) operations in the head of bridge rules, it should be clear that Reactive ASP as described in @cite_38 can be captured by .