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Published 2014 | public
Journal Article

Sequential Decision Making in Computational Sustainability Through Adaptive Submodularity

Abstract

Many problems in computational sustainability require making a sequence of decisions in complex, uncertain environments. Such problems are generally notoriously difficult. In this article, we review the recently discovered notion of adaptive submodularity, an intuitive diminishing returns condition that generalizes the classical notion of submodular set functions to sequential decision problems. Problems exhibiting the adaptive submodularity property can be efficiently and provably near-optimally solved using simple myopic policies. We illustrate this concept in several case studies of interest in computational sustainability: First, we demonstrate how it can be used to efficiently plan for resolving uncertainty in adaptive management scenarios. Secondly, we show how it applies to dynamic conservation planning for protecting endangered species, a case study carried out in collaboration with the US Geological Survey and the US Fish and Wildlife Service.

Additional Information

© 2014 Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. This research was partially supported by ONR grant N00014-09-1-1044, NSF grants CNS-0932392 and IIS-0953413, ERC StG 307036, the Caltech Center for the Mathematics of Information, a Microsoft Research Faculty Fellowship and by the U.S. Fish and Wildlife Service. We thank J. Bakker, J. Bush, M. Jensen, T. Kaye, J. Kenagy, C. Langston, S. Pearson, M. Singer, D. Stinson, D. Stokes, T. Thomas, B. Gardner, S. Morey, I. Bogunovic, D. Ray, and C. Camerer for their contributions.

Additional details

Created:
August 19, 2023
Modified:
October 26, 2023