Automated Aggregator -- Rewriting with the Counting Aggregate

TL;DR

The paper introduces Automated Aggregator (AAgg), a system that automatically rewrites logic programs into multiple equivalent forms to enhance solver performance and automate encoding selection in answer set programming.

Contribution

It presents AAgg, a novel automated rewriting system that generates diverse equivalent encodings to improve answer set programming solver efficiency.

Findings

AAgg produces multiple equivalent program variants.

Rewritten encodings show complementary performance.

System enhances automated solver selection.

Abstract

Answer set programming is a leading declarative constraint programming paradigm with wide use for complex knowledge-intensive applications. Modern answer set programming languages support many equivalent ways to model constraints and specifications in a program. However, so far answer set programming has failed to develop systematic methodologies for building representations that would uniformly lend well to automated processing. This suggests that encoding selection, in the same way as algorithm selection and portfolio solving, may be a viable direction for improving performance of answer-set solving. The necessary precondition is automating the process of generating possible alternative encodings. Here we present an automated rewriting system, the Automated Aggregator or AAgg, that given a non-ground logic program, produces a family of equivalent programs with complementary performance when run under modern answer set programming solvers. We demonstrate this behavior through experimental analysis and propose the system's use in automated answer set programming solver selection tools.