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
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Among others, (1998) presented an approach for updating logic programs with sets of rules based on dynamic logic programs. We syntactically redefine dynamic logic programs and investigate their semantical properties, looking at them from perspectives such as a belief revision and abstract consequence relation view. Since the approach does not respect minimality of change, we refine its stable model semantics and present minimal stable models and strict stable models. We also compare the update approach to related work, and find that is equivalent to a class of inheritance programs independently defined by (1999).