Introduction to algorithms
Solving very large weakly coupled Markov decision processes
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
The Sample Average Approximation Method for Stochastic Discrete Optimization
SIAM Journal on Optimization
Computational Optimization and Applications
A Clustering Approach to Solving Large Stochastic Matching Problems
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
A sparse sampling algorithm for near-optimal planning in large Markov decision processes
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Max-norm projections for factored MDPs
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Model minimization in Markov decision processes
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Tractable inference for complex stochastic processes
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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Real-world distributed systems and networks are often unreliable and subject to random failures of its components. Such a stochastic behavior affects adversely the complexity of optimization tasks performed routinely upon such systems. In this work we investigate Monte Carlo solutions for a class of two-stage optimization problems in stochastic networks in which the expected value of resources allocated before and after the occurence of stochastic failures needs to be optimized. The limitation of these problems is that their exact solutions are exponential in the number of unreliable network components: thus, exact methods do not scale-up well to large networks often seen in practice. We first show that Monte Carlo optimization methods can overcome the exponential bottleneck of exact methods. Next we support our theoretical findings on resource allocation experiments and show a very good scale-up potential of the methods on problems with large stochastic networks.