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
Artificial Intelligence - special issue on computational tradeoffs under bounded resources
Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
Heavy-Tailed Phenomena in Satisfiability and Constraint Satisfaction Problems
Journal of Automated Reasoning
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
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
Heuristics based on unit propagation for satisfiability problems
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
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
Summarizing CSP hardness with continuous probability distributions
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
Randomised restarted search in ILP
Machine Learning
Propositional Satisfiability and Constraint Programming: A comparative survey
ACM Computing Surveys (CSUR)
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
Planning and Scheduling the Operation of a Very Large Oil Pipeline Network
CP '08 Proceedings of the 14th international conference on Principles and Practice of Constraint Programming
Model Restarts for Structural Symmetry Breaking
CP '08 Proceedings of the 14th international conference on Principles and Practice of Constraint Programming
Restart Strategy Selection Using Machine Learning Techniques
SAT '09 Proceedings of the 12th International Conference on Theory and Applications of Satisfiability Testing
Problem-Sensitive Restart Heuristics for the DPLL Procedure
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
Efficient Multi-start Strategies for Local Search Algorithms
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Restart schedules for ensembles of problem instances
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Solution-guided multi-point constructive search for job shop scheduling
Journal of Artificial Intelligence Research
The effect of restarts on the efficiency of clause learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Taming the Complexity of Inductive Logic Programming
SOFSEM '10 Proceedings of the 36th Conference on Current Trends in Theory and Practice of Computer Science
ParamILS: an automatic algorithm configuration framework
Journal of Artificial Intelligence Research
On universal restart strategies for backtracking search
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Adaptive restart strategies for conflict driven SAT solvers
SAT'08 Proceedings of the 11th international conference on Theory and applications of satisfiability testing
Diversification and intensification in parallel SAT solving
CP'10 Proceedings of the 16th international conference on Principles and practice of constraint programming
Algorithm selection as a bandit problem with unbounded losses
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
Algorithms and mechanisms for procuring services with uncertain durations using redundancy
Artificial Intelligence
Efficient multi-start strategies for local search algorithms
Journal of Artificial Intelligence Research
Dynamic symmetry breaking restarted
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
Zchaff2004: an efficient SAT solver
SAT'04 Proceedings of the 7th international conference on Theory and Applications of Satisfiability Testing
Speedup techniques utilized in modern SAT solvers
SAT'05 Proceedings of the 8th international conference on Theory and Applications of Satisfiability Testing
Efficient detection of errors in java components using random environment and restarts
TACAS'10 Proceedings of the 16th international conference on Tools and Algorithms for the Construction and Analysis of Systems
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We describe theoretical results and empirical study of context-sensitive restart policies for randomized search procedures. The methods generalize previous results on optimal restart policies by exploiting dynamically updated beliefs about the probability distribution for run time. Rather than assuming complete knowledge or zero knowledge about the run-time distribution, we formulate restart policies that consider real-time observations about properties of instances and the solver's activity. We describe background work on the application of Bayesian methods to build predictive models for run time, introduce an optimal policy for dynamic restarts that considers predictions about run time, and perform a comparative Study of traditional fixed versus dynamic restart policies.