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.
Several systems and frameworks for modeling the dynamics of knowledge and the flow of information have been developed. These systems are obviously related to reactive multi-context systems. In this section we provide a comparison with those approaches we consider most relevant, stressing differences as well as commonalities with . Note that we focus entirely on dynamic approaches and do not include systems which handle heterogeneous information in a static setting. An interesting example of the latter type are Lierler and Truszczy 'nski's abstract modular systems @cite_12 . In a nutshell, modular systems realize the communication between different reasoning modules through joint vocabularies rather than bridge rules. These systems are best viewed as alternatives to classical MCSs. It is an open question how to adapt them to a dynamic setting.
@cite_12
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Integrating diverse formalisms into modular knowledge representation systems offers increased expressivity, modeling convenience, and computational benefits. We introduce the concepts of abstract inference modules and abstract modular inference systems to study general principles behind the design and analysis of model generating programs, or solvers, for integrated multi-logic systems. We show how modules and modular systems give rise to transition graphs, which are a natural and convenient representation of solvers, an idea pioneered by the SAT community. These graphs lend themselves well to extensions that capture such important solver design features as learning. In the paper, we consider two flavors of learning for modular formalisms, local and global. We illustrate our approach by showing how it applies to answer set programming, propositional logic, multi-logic systems based on these two formalisms and, more generally, to satisfiability modulo theories.