A Bayesian Approach to Tackling Hard Computational Problems
Abstract
We are developing a general framework for using learned Bayesian models for decision-theoretic control of search and reasoningalgorithms. We illustrate the approach on the specific task of controlling both general and domain-specific solvers on a hard class of structured constraint satisfaction problems. A successful strategyfor reducing the high (and even infinite) variance in running time typically exhibited by backtracking search algorithms is to cut off and restart the search if a solution is not found within a certainamount of time. Previous work on restart strategies have employed fixed cut off values. We show how to create a dynamic cut off strategy by learning a Bayesian model that predicts the ultimate length of a trial based on observing the early behavior of the search algorithm. Furthermore, we describe the general conditions under which a dynamic restart strategy can outperform the theoretically optimal fixed strategy.
Cite
@article{arxiv.1301.2279,
title = {A Bayesian Approach to Tackling Hard Computational Problems},
author = {Eric J. Horvitz and Yongshao Ruan and Carla P. Gomes and Henry Kautz and Bart Selman and David Maxwell Chickering},
journal= {arXiv preprint arXiv:1301.2279},
year = {2013}
}
Comments
Appears in Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI2001)