Sequential decision making in computational sustainability via adaptive submodularity

AI Magazine
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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 publication details

Publication type Article
Publication Subtype Journal Article
Title Sequential decision making in computational sustainability via adaptive submodularity
Series title AI Magazine
DOI 10.1609/aimag.v35i2.2526
Volume 35
Issue 2
Year Published 2015
Language English
Publisher American Association for Artificial Intelligence
Publisher location La Canada, CA
Contributing office(s) Patuxent Wildlife Research Center
Description 11 p.
First page 8
Last page 18
Online Only (Y/N) N
Additional Online Files (Y/N) N
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