Optimal speedup of Las Vegas algorithms
Information Processing Letters
Easy problems are sometimes hard
Artificial Intelligence
Phase transitions and the search problem
Artificial Intelligence - Special volume on frontiers in problem solving: phase transitions and complexity
Boosting combinatorial search through randomization
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
Neuro-Dynamic Programming
Heavy-Tailed Phenomena in Satisfiability and Constraint Satisfaction Problems
Journal of Automated Reasoning
A Bayesian Approach to Tackling Hard Computational Problems
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Generating Satisfiable Problem Instances
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Formal Models of Heavy-Tailed Behavior in Combinatorial Search
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
Heuristics based on unit propagation for satisfiability problems
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Balance and filtering in structured satisfiable problems
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Pushing the envelope: planning, propositional logic, and stochastic search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Problem structure in the presence of perturbations
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
A Bayesian approach to learning Bayesian networks with local structure
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Analysis of Restart Mechanisms in Software Systems
IEEE Transactions on Software Engineering
Discrete Applied Mathematics
Understanding the role of noise in stochastic local search: Analysis and experiments
Artificial Intelligence
Strategies for Solving SAT in Grids by Randomized Search
Proceedings of the 9th AISC international conference, the 15th Calculemas symposium, and the 7th international MKM conference on Intelligent Computer Mathematics
Empirical hardness models: Methodology and a case study on combinatorial auctions
Journal of the ACM (JACM)
Restart Strategy Selection Using Machine Learning Techniques
SAT '09 Proceedings of the 12th International Conference on Theory and Applications of Satisfiability Testing
Low-knowledge algorithm control
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Restart schedules for ensembles of problem instances
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
On universal restart strategies for backtracking search
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Journal of Automated Reasoning
Experimental analysis of the correlation of HTTP GET invocations
EPEW'06 Proceedings of the Third European conference on Formal Methods and Stochastic Models for Performance Evaluation
A measurement study of the interplay between application level restart and transport protocol
ISAS'04 Proceedings of the First international conference on Service Availability
| Hi-index | 0.00 |
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.