Playing games against nature: optimal policies for renewable resource allocation

Stefano Ermon, Jon Conrad, Carla Gomes, Bart Selman
Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:184-192, 2010.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR8-ermon10a, title = {Playing games against nature: optimal policies for renewable resource allocation}, author = {Ermon, Stefano and Conrad, Jon and Gomes, Carla and Selman, Bart}, booktitle = {Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence}, pages = {184--192}, year = {2010}, editor = {Grünwald, Peter and Spirtes, Peter}, volume = {R8}, series = {Proceedings of Machine Learning Research}, month = {08--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r8/main/assets/ermon10a/ermon10a.pdf}, url = {https://proceedings.mlr.press/r8/ermon10a.html}, abstract = {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.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Playing games against nature: optimal policies for renewable resource allocation %A Stefano Ermon %A Jon Conrad %A Carla Gomes %A Bart Selman %B Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2010 %E Peter Grünwald %E Peter Spirtes %F pmlr-vR8-ermon10a %I PMLR %P 184--192 %U https://proceedings.mlr.press/r8/ermon10a.html %V R8 %X 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. %Z Reissued by PMLR on 04 October 2026.
APA
Ermon, S., Conrad, J., Gomes, C. & Selman, B.. (2010). Playing games against nature: optimal policies for renewable resource allocation. Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R8:184-192 Available from https://proceedings.mlr.press/r8/ermon10a.html. Reissued by PMLR on 04 October 2026.

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