@inproceedings{189965f6e51c43208689d6e9e7916cbb,
title = "Multiobjective optimization for the stochastic physical search problem",
abstract = "We model an intelligence collection activity as multiobjective optimization on a binary stochastic physical search problem, providing formal definitions of the problem space and nondominated solution sets. We present the Iterative Domination Solver as an approximate method for generating solution sets that can be used by a human decision maker to meet the goals of a mission. We show that our approximate algorithm performs well across a range of uncertainty parameters, with orders of magnitude less execution time than existing solutions on randomly generated instances.",
keywords = "Multiobjective optimization, Path planning, Planning under uncertainty, Stochastic search",
author = "Jeffrey Hudack and Nathaniel Gemelli and Daniel Brown and Steven Loscalzo and Oh, {Jae C.}",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2015 ; Conference date: 10-06-2015 Through 12-06-2015",
year = "2015",
doi = "10.1007/978-3-319-19066-2_21",
language = "English (US)",
isbn = "9783319190655",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "212--221",
editor = "Chang-Hwan Lee and Yongdai Kim and Kwon, {Young Sig} and Juntae Kim and Moonis Ali",
booktitle = "Current Approaches in Applied Artificial Intelligence - 28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2015, Proceedings",
}