Encoding Selection for Solving Hamiltonian Cycle Problems with ASP

TL;DR

This paper demonstrates that machine learning can be used to select the most effective encoding for solving Hamiltonian cycle problems with ASP, leading to improved performance over traditional methods.

Contribution

It introduces a method to predict encoding performance using machine learning, enabling better encoding selection for ASP solving of Hamiltonian cycle problems.

Findings

Encoding selection improves ASP solving efficiency.

Predictive models accurately estimate encoding performance.

Significant performance gains achieved through learned encoding selection.

Abstract

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.