A model building framework for Answer Set Programming with external computations
This addresses scalability issues in real-world applications of logic programming with external sources, such as in connected software systems, though it is incremental as it builds on existing HEX programs.
The paper tackled the challenge of efficiently evaluating nonmonotonic logic programs with external source access by introducing a configurable framework that divides programs into overlapping evaluation units, interleaving external computation and model building, resulting in a prototype that shows significant improvement over previous approaches.
As software systems are getting increasingly connected, there is a need for equipping nonmonotonic logic programs with access to external sources that are possibly remote and may contain information in heterogeneous formats. To cater for this need, HEX programs were designed as a generalization of answer set programs with an API style interface that allows to access arbitrary external sources, providing great flexibility. Efficient evaluation of such programs however is challenging, and it requires to interleave external computation and model building; to decide when to switch between these tasks is difficult, and existing approaches have limited scalability in many real-world application scenarios. We present 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. The latter will be processed by interleaving external evaluation and model building using an evaluation graph and a model graph, respectively, and by combining intermediate results. Experiments with our prototype implementation show a significant improvement compared to previous approaches. While designed for HEX-programs, the new evaluation approach may be deployed to related rule-based formalisms as well.