Uncertainty in Artificial Intelligence
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Algorithm Portfolio Design: Theory vs. Practice
Carla Gomes, Bart Selman
Abstract:
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 detailed evaluation of the portfolio approach on distributions of hard combinatorial search problems. We show under what conditions the protfolio approach can have a dramatic computational advantage over the best traditional methods.
Keywords:
Pages: 190-197
PS Link:
PDF Link: /papers/97/p190-gomes.pdf
BibTex:
@INPROCEEDINGS{Gomes97,
AUTHOR = "Carla Gomes and Bart Selman",
TITLE = "Algorithm Portfolio Design: Theory vs. Practice",
BOOKTITLE = "Proceedings of the Thirteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-97)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1997",
PAGES = "190--197"
}


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