Automated Aggregator -- Rewriting with the Counting Aggregate
This work addresses the problem of encoding selection for answer set programming users, offering an incremental improvement by automating the generation of alternative encodings to enhance solver performance.
The paper tackles the lack of systematic methodologies for generating alternative encodings in answer set programming, which hinders automated processing and performance. It presents the Automated Aggregator (AAgg), a system that produces equivalent programs with complementary performance, as demonstrated through experimental analysis.
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.