The DMCS system is an implementation of the equilibrium semantics for heterogeneous and nonmonotonic multi-context systems (MCS) [3], which feature contexts with heterogeneous and possibly nonmonotonic logics. Each context in an MCS comprises of two parts: a local knowledge base and a set of bridge rules that can access the beliefs of

Nonmonotonic multi-context systems (MCS) provide a formalism to represent knowledge exchange between heterogeneous and possibly nonmonotonic knowledge bases (contexts). Recent advancements to evaluate MCS semantics (given in terms of so-called equilibria) enable their application to realistic and fully distributed scenarios of knowledge exchange. However, the current MCS formalism cannot handle open environments, i.e., when knowledge sources and their contents may change over time and are not known a priori. To improve on this aspect, we develop Dynamic Nonmonotonic Multi-Context Systems, which consist of schematic contexts that allow to leave part of the information interlinkage open at design time. A concrete interlinking is established by a configuration step at run time, where concrete contexts and information imports between them are fixed. We formally develop a corresponding extension and provide semantics by instantiation to ordinary MCS. Furthermore, we develop a basic distributed configuration algorithm and discuss several refinements that affect the resulting configurations, in particular by means of optimizations according to different quality criteria. This discussion is complemented with experimental results obtained with a corresponding prototype implementation. 1

Abstract. Multi-Context Systems (MCS) are instances of a nonmonotonic formalism for interlinking heterogeneous knowledge bases in a way such that the information flow is in equilibrium. Recently, algorithms for evaluating distributed MCS have been proposed which compute global system models, called equilibria, by local computation and model exchange. Unfortunately, they suffer from a bottleneck that stems from the way models are exchanged, which limits the applicability to situations with small information interfaces. To push MCS to more realistic and practical scenarios, we present a novel algorithm that computes at most k ≥ 1 models of an MCS using asynchronous communication. Models are wrapped into packages, and contexts in an MCS continuously stream packages to generate at most k models at the root of the system. We have implemented this algorithm in a new solver for distributed MCS, and show promising experimental results. 1

Abstract. Heterogeneous nonmonotonic multi-context systems (MCS) permit different logics to be used in different contexts, and link them via bridge rules. We investigate the role of symmetry detection and symmetry breaking in such systems to eliminate symmetric parts of the search space and, thereby, simplify the evaluation process. We propose a distributed algorithm that takes a local stance, i.e., computes independently the partial symmetries of a context and, in order to construct potential symmetries of the whole, combines them with those partial symmetries returned by neighbouring contexts. We prove the correctness of our methods. We instantiate such symmetry detection and symmetry breaking in a multi-context system with contexts that use answer set programs, and demonstrate computational benefit on some recently proposed benchmarks. 1

In the context of answer set programming, this work investigates symmetry detection and symmetry breaking to eliminate symmetric parts of the search space and, thereby, simplify the solution process. We contribute a reduction of symmetry detection to a graph automorphism problem which allows to extract symmetries of a logic program from the symmetries of the constructed coloured graph. The correctness of our reduction is rigorously proven. We also propose an encoding of symmetry-breaking constraints in terms of permutation cycles and use only generators in this process which implicitly represent symmetries and always with exponential compression. These ideas are formulated as preprocessing and implemented in a completely automated flow that first detects symmetries from a given answer set program, adds symmetry-breaking constraints, and can be applied to any existing answer set solver. We demonstrate computational impact on benchmarks versus direct application of the solver. Furthermore, we explore symmetry breaking for answer set programming in two domains: first, constraint answer set programming as a novel approach to represent and solve constraint satisfaction problems, and second, distributed nonmonotonic multi-context systems.