In this paper, we introduce reactive multi-context systems (rMCSs), a framework for reactive reasoning in the presence of heterogeneous knowledge sources. In particular, we show how to integrate data streams into multi-context systems (MCSs) and how to model the dynamics of the systems, based on two types of bridge rules. We illustrate how several typical problems arising in the context of stream reasoning can be handled using our framework. Reasoning based on multiple knowledge sources that need to be integrated faces the problem of potential inconsistencies. We discuss various methods for handling inconsistencies, with a special focus on non-existence of equilibria. In particular, we show how methods developed for managed MCSs can be generalized to rMCSs. We also study the issue of nondeterminism in rMCSs. One way of avoiding nondeterminism is by applying an alternative, skeptical semantics. We show how such a semantics, called well-founded semantics, can be defined for rMCSs, and what the effect of using this semantics instead of the original one is. We investigate the complexity of various reasoning problems related to rMCSs. Finally, we discuss related work, with a special focus on two of the most relevant approaches w.r.t. stream reasoning, namely LARS and STARQL.
from cs.AI updates on arXiv.org http://ift.tt/2cSD4Pr
via IFTTT
No comments:
Post a Comment