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.
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 .
@cite_38 @cite_21
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We introduce a comprehensive framework for computing diverse (or similar) solutions to logic programs with preferences. Our framework provides a wide spectrum of complete and incomplete methods for solving this task. Apart from proposing several new methods, it also accommodates existing ones and generalizes them to programs with preferences. Interestingly, this is accomplished by integrating and automating several basic ASP techniques - being of general interest even beyond diversification. The enabling factor of this lies in the recent advance of multi-shot ASP solving that provides us with fine-grained control over reasoning processes and abolishes the need for solver modifications and wrappers that were indispensable in previous approaches. Our framework is implemented as an extension to the ASP-based preference handling system asprin. We use the resulting system asprin 2 for an empirical evaluation of the diversification methods comprised in our framework. 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.