System Predictor: Grounding Size Estimator for Logic Programs under Answer Set Semantics
This work addresses a domain-specific challenge in answer set programming for developers and researchers, offering an incremental improvement in program optimization.
The paper tackles the problem of selecting the best-performing version of logic programs in answer set programming by introducing System Predictor, a tool that estimates grounding size to guide program rewritings. The results show potential for improving performance through this approach.
Answer set programming is a declarative logic programming paradigm geared towards solving difficult combinatorial search problems. While different logic programs can encode the same problem, their performance may vary significantly. It is not always easy to identify which version of the program performs the best. We present the system Predictor (and its algorithmic backend) for estimating the grounding size of programs, a metric that can influence a performance of a system processing a program. We evaluate the impact of Predictor when used as a guide for rewritings produced by the answer set programming rewriting tools Projector and Lpopt. The results demonstrate potential to this approach.