Article
A Machine Learning System to Improve the Performance of ASP Solving Based on Encoding Selection
Proceedings of the 16th International Conference on Logic Programming and Non-monotonic Reasoning
(2022)
Abstract
Answer set programming (ASP) has long been used for modeling and solving hard search problems. Experience shows that the performance of ASP tools on different ASP encodings of the same problem may vary greatly from instance to instance and it is rarely the case that one encoding outperforms all others. We describe a system and its implementation that given a set of encodings and a training set of instances, builds performance models for the encodings, predicts the execution time of these encodings on new instances, and uses these predictions to select an encoding for solving.
Disciplines
Publication Date
2022
Citation Information
Liu Liu, Miroslaw Truszczyński and Yuliya Lierler. "A Machine Learning System to Improve the Performance of ASP Solving Based on Encoding Selection" Proceedings of the 16th International Conference on Logic Programming and Non-monotonic Reasoning (2022) Available at: http://works.bepress.com/yuliya_lierler/119/