Modular answer set solving

TL;DR: This work presents a new approach for the evaluation of logic programs with external source access, which is based on a configurable framework for dividing the non-ground program into possibly overlapping smaller parts called evaluation units.

TL;DR: These results show that the model-based modular systems framework offers a simple unifying framework for studies of modularity in knowledge representation, and recently introduced modular knowledge representation formalisms integrating logic programming with satisfiability and, more generally, with constraint satisfaction can be cast as modular systems in this sense.

TL;DR: The concepts of abstract inference modules and abstract modular inference systems are introduced to study general principles behind the design and analysis of model-generating programs, or solvers, for integrated multi-logic systems.

TL;DR: The authors introduce the concept of conditional syntax splitting, inspired by the notion of conditional independence as known from probability theory, and show that lexicographic inference and system W satisfy syntax splitting and connect it to several known properties from the literature on non-monotonic reasoning.

TL;DR: A novel module theorem is established which enables the decomposition of DLP-functions given their strongly connected components based on positive dependencies induced by rules and the concept of modular equivalence is introduced for the mutual comparison of DLPs using a generalization of a translation-based verification method.

TL;DR: In this article, the authors define the notion of a disjunctive logic function and establish a novel module theorem which indicates the compositionality of stable-model semantics for DLP-functions.

TL;DR: It is proved that, even with individual modules being polytime solvable, the framework is expressive enough to capture all of NP, a property which does not hold without loop.

TL;DR: A module-based framework for constraint modeling where it is possible to combine different constraint modeling languages and exploit their strengths in a flexible way and enables the use of alternative semantical underpinnings such as default negation and classical negation in the same model.