Consider the problem of protecting endangered species by selecting patches of land to be used for conservation purposes. Typically, the availability of patches changes over time, and recommendations must be made adaptively. This is a challenging prototypical example of sequential optimization problems under uncertainty in computational sustainability. Existing techniques do not scale to problems of realistic size. In this paper, we develop an efficient algorithm for adaptively making recommendations for conservation planning, and prove that it obtains near-optimal performance. We further evaluate our approach on a detailed reserve design case study of conservation planning for three rare taxa in the Pacific Northwest of the United States.

@inproceedings{golovin11reserve,
	author = {Daniel Golovin and Andreas Krause and Beth Gardner and Sarah Converse and Steve Morey},
	booktitle = {Conference on Artificial Intelligence (AAAI)},
	title = {Dynamic Resource Allocation in Conservation Planning},
	year = {2011}}