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ABSTRACT
Q-Learning Multi-Objective Sequential Optimal Sensor Parameter Weights
The goal of our solution is to deliver trustworthy decision making
analysis tools which evaluate situations and potential impacts of
such decisions through acquired information and add efficiency for
continuing mission operations and analyst information.We discuss
the use of cooperation in modeling and simulation and show
quantitative results for design choices to resource allocation. The
key contribution of our paper is to combine remote sensing decision
making with Nash Equilibrium for sensor parameter weighting
optimization. By calculating all Nash Equilibrium possibilities per
period, optimization of sensor allocation is achieved for overall
higher system efficiency. Our tool provides insight into what are the
most important or optimal weights for sensor parameters and can be
used to efficiently tune those weights.
Full Text
