A Randomized Parallel Backtracking Algorithm
IEEE Transactions on Computers
Parallel depth first search. Part I. implementation
International Journal of Parallel Programming
Parallel depth first search. Part II. analysis
International Journal of Parallel Programming
Artificial Intelligence
Do the right thing: studies in limited rationality
Do the right thing: studies in limited rationality
Efficient resource-bounded reasoning in AT-RALPH
Proceedings of the first international conference on Artificial intelligence planning systems
Optimal speedup of Las Vegas algorithms
Information Processing Letters
Deliberation scheduling for problem solving in time-constrained environments
Artificial Intelligence
Operational rationality through compilation of anytime algorithms
Operational rationality through compilation of anytime algorithms
Boosting combinatorial search through randomization
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Optimal schedules for monitoring anytime algorithms
Artificial Intelligence - special issue on computational tradeoffs under bounded resources
On the Efficiency of Parallel Backtracking
IEEE Transactions on Parallel and Distributed Systems
Optimal Parallelization of Las Vegas Algorithms
STACS '94 Proceedings of the 11th Annual Symposium on Theoretical Aspects of Computer Science
Optimal schedules for parallelizing anytime algorithms: the case of independent processes
Eighteenth national conference on Artificial intelligence
Algorithm portfolio design: theory vs. practice
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Optimal schedules for parallelizing anytime algorithms: the case of independent processes
Eighteenth national conference on Artificial intelligence
Utility-based multi-agent system for performing repeated navigation tasks
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Optimal schedules for parallelizing anytime algorithms: the case of shared resources
Journal of Artificial Intelligence Research
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
Algorithm portfolio selection as a bandit problem with unbounded losses
Annals of Mathematics and Artificial Intelligence
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The performance of anytime algorithms having a nondeterministic nature can be improved by solving simultaneously several instances of the algorithm-problem pairs. These pairs may include different instances of a problem (like starting from a different initial state), different algorithms (if several alternatives exist), or several instances of the same algorithm (for nondeterministic algorithms).In this paper we present a general framework for optimal parallelization of independent processes. We show a mathematical model for this framework, present algorithms for optimal scheduling, and demonstrate its usefulness on a real problem.