Explanation-based learning: a problem solving perspective
Artificial Intelligence
Planning and control
Optimal speedup of Las Vegas algorithms
Information Processing Letters
A filtering algorithm for constraints of difference in CSPs
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Decision-theoretic troubleshooting
Communications of the ACM
Boosting combinatorial search through randomization
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
A machine program for theorem-proving
Communications of the ACM
Heavy-Tailed Phenomena in Satisfiability and Constraint Satisfaction Problems
Journal of Automated Reasoning
Ideal reformulation of belief networks
UAI '90 Proceedings of the Sixth Annual Conference on 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
Unifying SAT-based and graph-based planning
IJCAI'99 Proceedings of the 16th 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
Integrating abstraction and explanation-based learning in PRODIGY
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
Using CSP look-back techniques to solve real-world SAT instances
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
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
Reasoning, metareasoning, and mathematical truth: studies of theorem proving under limited resources
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
A Bayesian approach to learning Bayesian networks with local structure
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Learning the Empirical Hardness of Optimization Problems: The Case of Combinatorial Auctions
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
A learning-based algorithm selection meta-reasoner for the real-time MPE problem
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
A decision theoretic meta-reasoner for constraint optimization
AI'05 Proceedings of the 18th Canadian Society conference on Advances in Artificial Intelligence
Evaluating component solver contributions to portfolio-based algorithm selectors
SAT'12 Proceedings of the 15th international conference on Theory and Applications of Satisfiability Testing
Learning algorithm portfolios for parallel execution
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
Algorithm runtime prediction: Methods & evaluation
Artificial Intelligence
Heuristic search when time matters
Journal of Artificial Intelligence Research
Simple algorithm portfolio for SAT
Artificial Intelligence Review
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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 time required 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 review 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.