Non-Submodular Maximization via the Greedy Algorithm and the Effects of Limited Information in Multi-Agent Execution
Author:
Affiliation:
1. Virginia Tech,Department of Electrical and Computer Engineering,Blacksburg,VA,USA
2. US Naval Research Laboratory, Acoustics Division,Washington D.C.,USA,7130
Funder
Office of Naval Research
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9981026/9981028/09982070.pdf?arnumber=9982070
Reference22 articles.
1. Greed is still good: maximizing monotone sub-modular+ supermodular (bp) functions;bai;International Conference on Machine Learning,2018
2. Distributed Submodular Maximization on Partition Matroids for Planning on Large Sensor Networks
3. Guar-antees for greedy maximization of non-submodular functions with ap-plications;bian;International Conference on Machine Learning,2017
4. Acquiring environmental information yields better anticipated search performance
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