Encoding Selection for Solving Hamiltonian Cycle Problems with ASP
This work addresses the challenge of encoding selection in ASP for combinatorial problems like Hamiltonian cycles, offering an incremental improvement through learned predictors.
The paper tackles the problem of selecting among alternative Answer Set Programming (ASP) encodings for the Hamiltonian cycle problem, using machine learning to predict and choose the best encoding per instance, resulting in significant performance gains.
It is common for search and optimization problems to have alternative equivalent encodings in ASP. Typically none of them is uniformly better than others when evaluated on broad classes of problem instances. We claim that one can improve the solving ability of ASP by using machine learning techniques to select encodings likely to perform well on a given instance. We substantiate this claim by studying the hamiltonian cycle problem. We propose several equivalent encodings of the problem and several classes of hard instances. We build models to predict the behavior of each encoding, and then show that selecting encodings for a given instance using the learned performance predictors leads to significant performance gains.