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.
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Hudack, J., Gemelli, N., Brown, D., Loscalzo, S., Oh, J.C. (2015). Multiobjective Optimization for the Stochastic Physical Search Problem. In: Ali, M., Kwon, Y., Lee, CH., Kim, J., Kim, Y. (eds) Current Approaches in Applied Artificial Intelligence. IEA/AIE 2015. Lecture Notes in Computer Science(), vol 9101. Springer, Cham. https://doi.org/10.1007/978-3-319-19066-2_21
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DOI: https://doi.org/10.1007/978-3-319-19066-2_21
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