UKY-Computer Science Keeping Current 2019-03-20 - Liu Liu, Daniel Houston and Michael Dingess

Abstract: Representing a problem in a declarative programming formalism such as answer set programming (ASP) is not easy. Although modeling the constraints is often easy, selecting a representation that always yields good performance is hard. Equivalent representations often perform differently on a different problem instances. In this talk, we discuss selecting among equivalent problem encodings one that is likely to perform the best for a given instance. We discuss applying machine learning to help us make the selection, as well as automating the task of generating alternative equivalent problem encodings. The talk consists of three parts, presenting different but closely related projects,