
Sequential Decision Making in Computational Sustainability Through Adaptive Submodularity
Author(s) -
Krause Andreas,
Golovin Daniel,
Converse Sarah
Publication year - 2014
Publication title -
ai magazine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.597
H-Index - 79
eISSN - 2371-9621
pISSN - 0738-4602
DOI - 10.1609/aimag.v35i2.2526
Subject(s) - computer science , adaptive management , submodular set function , sustainability , set (abstract data type) , simple (philosophy) , plan (archaeology) , service (business) , mathematical optimization , environmental resource management , mathematics , ecology , geography , economics , philosophy , epistemology , biology , programming language , economy , archaeology
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. Then, we show how it applies to dynamic conservation planning for protecting endangered species, a case study carried out in collaboration with the U.S. Geological Survey and the U.S. Fish and Wildlife Service.