2 research outputs found
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.Comment: In Proceedings ICLP 2019, arXiv:1909.0764
Automated Aggregator -- Rewriting with the Counting Aggregate
Answer set programming is a leading declarative constraint programming
paradigm with wide use for complex knowledge-intensive applications. Modern
answer set programming languages support many equivalent ways to model
constraints and specifications in a program. However, so far answer set
programming has failed to develop systematic methodologies for building
representations that would uniformly lend well to automated processing. This
suggests that encoding selection, in the same way as algorithm selection and
portfolio solving, may be a viable direction for improving performance of
answer-set solving. The necessary precondition is automating the process of
generating possible alternative encodings. Here we present an automated
rewriting system, the Automated Aggregator or AAgg, that given a non-ground
logic program, produces a family of equivalent programs with complementary
performance when run under modern answer set programming solvers. We
demonstrate this behavior through experimental analysis and propose the
system's use in automated answer set programming solver selection tools.Comment: In Proceedings ICLP 2020, arXiv:2009.0915