Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Constraint-based reasoning
Phase transitions and the search problem
Artificial Intelligence - Special volume on frontiers in problem solving: phase transitions and complexity
New methods to color the vertices of a graph
Communications of the ACM
"Go with the winners" algorithms
SFCS '94 Proceedings of the 35th Annual Symposium on Foundations of Computer Science
Automatic generation of some results in finite algebra
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
Where the really hard problems are
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Hard and easy distributions of SAT problems
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Using deep structure to locate hard problems
AAAI'92 Proceedings of the tenth national conference on 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
Which search problems are random?
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
An Overview of Backtrack Search Satisfiability Algorithms
Annals of Mathematics and Artificial Intelligence
Heavy-Tailed Phenomena in Satisfiability and Constraint Satisfaction Problems
Journal of Automated Reasoning
Cooperative Strategies for Solving the Bicriteria Sparse Multiple Knapsack Problem
Journal of Heuristics
Using Randomization and Learning to Solve Hard Real-World Instances of Satisfiability
CP '02 Proceedings of the 6th International Conference on Principles and Practice of Constraint Programming
Systematic vs. Local Search for SAT
KI '99 Proceedings of the 23rd Annual German Conference on Artificial Intelligence: Advances in Artificial Intelligence
Optimal schedules for parallelizing anytime algorithms: the case of independent processes
Eighteenth national conference on Artificial intelligence
Search Strategies for Hybrid Search Spaces
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
SAT problems with chains of dependent variables
Discrete Applied Mathematics - The renesse issue on satisfiability
The Dynamic Selection of Coordination Mechanisms
Autonomous Agents and Multi-Agent Systems
Automated discovery of local search heuristics for satisfiability testing
Evolutionary Computation
An Algorithm Selection Approach for Simulation Systems
Proceedings of the 22nd Workshop on Principles of Advanced and Distributed Simulation
Large-Scale Design Space Exploration of SSA
CMSB '08 Proceedings of the 6th International Conference on Computational Methods in Systems Biology
Data mining for simulation algorithm selection
Proceedings of the 2nd International Conference on Simulation Tools and Techniques
Optimal schedules for parallelizing anytime algorithms: the case of shared resources
Journal of Artificial Intelligence Research
Boosting distributed constraint satisfaction
Journal of Heuristics
Evaluating las vegas algorithms: pitfalls and remedies
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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
Planning as satisfiability: Heuristics
Artificial Intelligence
A hybrid paradigm for adaptive parallel search
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
Learning algorithm portfolios for parallel execution
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
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Stochastic algorithms are among the best for solving computationally hard search and reasoning problems, The runtime of such procedures is characterized by a random variable. Different algorithms give rise to different probability distributions. One can take advantage of such differences by combining several algorithms into a portfolio, and running them in parallel or interleaving them on a single processor. We provide a de- ~ailed evaluation of the portfolio approach on distributions of hard combinatorial search problems. We show under what conditions the portfolio approach can have a dramatic computational advantage over the best traditional methods.