Encoding Selection for Solving Hamiltonian Cycle Problems with ASP
  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.
