Uncertainty in Artificial Intelligence
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Playing games against nature: optimal policies for renewable resource allocation
Stefano Ermon, Jon Conrad, Carla Gomes, Bart Selman
In this paper we introduce a class of Markov decision processes that arise as a natural model for many renewable resource allocation problems. Upon extending results from the inventory control literature, we prove that they admit a closed form solution and we show how to exploit this structure to speed up its computation. We consider the application of the proposed framework to several problems arising in very different domains, and as part of the ongoing effort in the emerging field of Computational Sustainability we discuss in detail its application to the Northern Pacific Halibut marine fishery. Our approach is applied to a model based on real world data, obtaining a policy with a guaranteed lower bound on the utility function that is structurally very different from the one currently employed.
Pages: 168-176
PS Link:
PDF Link: /papers/10/p168-ermon.pdf
AUTHOR = "Stefano Ermon and Jon Conrad and Carla Gomes and Bart Selman",
TITLE = "Playing games against nature: optimal policies for renewable resource allocation",
BOOKTITLE = "Proceedings of the Twenty-Sixth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-10)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2010",
PAGES = "168--176"

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