Yongshao Ruan

Restart Policies with Dependence among Runs: A Dynamic Programming Approach

The time required for a backtracking search procedure to solve a problem can be minimized by employing randomized restart procedures. To date, researchers designing restart policies have relied on the simplifying assumption that runs are probabilistically independent from one another. We relax the assumption of independence among runs and address the challenge of identifying an optimal restart policy for the dependent case. We show how offline dynamic programming can be used to generate an ideal restart policy, and how the policy can be used in conjunction with real-time observations to control the timing of restarts. We present results of experiments on applying the methods to create ideal restart policies for several challenging search problems using two different solvers.

Balance and Filtering in Structured Satisfiable Problems (Preliminary Report)

Abstract Abstract New methods to generate hard random problem instances have driven progress on algorithms for deduction and constraint satisfaction. Recently Achlioptas et al. (AAAI 2000) introduced a new generator based on Latin squares that creates only satisfiable problems, and so can be used to accurately test incomplete (one sided) solvers. We investigate how this and other generators are biased away from the uniform distribution of satisfiable problems and show how they can be improved by imposing a balance condition. More generally, we show that the generator is one member of a family of related models that generate distributions ranging from ones that are everywhere tractable to ones that exhibit a sharp hardness threshold. We also discuss the critical role of the problem encoding in the performance of both systematic and local search solvers.

A Bayesian Approach to Tackling Hard Computational Problems (Preliminary Report)

Abstract Abstract We describe research and results centering on the construction and use of Bayesian models that can predict the run time of problem solvers. Our efforts are motivated by observations of high variance in the run time uired to solve instances for several challenging problems. The methods have application to the decision-theoretic control of hard search and reasoning algorithms. We illustrate the approach with a focus on the task of predicting run time for general and domain-specific solvers on a hard class of structured constraint satisfaction problems. We describe the use of learned models to predict the ultimate length of a trial, based on observing the behavior of the search algorithm during an early phase of a problem session. Finally, we discuss how we can employ the models to inform dynamic run-time decisions. We thank Dimitris Achlioptas for his insightful contributions and feedback.