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.
The idea of EVOLP is that programs can update their own rules thus describing their possible self-evolution. Each self-evolution can be represented by a sequence of programs, each program corresponding to a state, and these sequences of programs can be treated as in Dynamic Logic Programs (DLPs) @cite_16 . Dynamic logic programs are sequences @math of generalized logic programs, whose semantics is based on the causal rejection principle. The idea is that the most recent rules are put in force, (where @math is to be seen as the most recent set of rules), and the previous rules are valid as far as possible, i.e., they are only kept if they do not conflict with more recent rules. Here, these intuitions about DLPs are sufficient, and we point to @cite_16 for more details.
@cite_16
2105631606
In this paper we investigate updates of knowledge bases represented by logic programs. In order to represent negative information, we use generalized logic programs which allow default negation not only in rule bodies but also in their heads. We start by introducing the notion of an update P U of one logic program P by another logic program U. Subsequently, we provide a precise semantic characterization of P U , and study some basic properties of program updates. In particular, we show that our update programs generalize the notion of interpretation update. We then extend this notion to compositional sequences of logic programs updates P1 P2 ; defining a dynamic program update, and thereby introducing the paradigm of dynamic logic programming. This paradigm significantly facilitates modularization of logic programming, and thus modularization of non-monotonic reasoning as a whole. Specifically, suppose that we are given a set of logic program modules, each describing a diAerent state of our knowledge of the world. DiAerent states may represent diAerent time points or diAerent sets of priorities or perhaps even diAerent viewpoints. Consequently, program modules may contain mutually contradictory as well as overlapping information. The role of the dynamic program update is to employ the mutual relationships existing between diAerent modules to precisely determine, at any given module composition stage, the declarative as well as the procedural semantics of the combined program resulting from the modules.  2000 Elsevier Science Inc. All rights reserved.