Abstract
Finding suitable jobs for US Navy sailors from time to time is an important and ever-changing process. An Intelligent Distribution Agent and particularly its constraint satisfaction module take up the challenge to automate the process. The constraint satisfaction module’s main task is to assign sailors to new jobs in order to maximize Navy and sailor happiness. We present various neural network techniques combined with several statistical criteria to optimize the module’s performance and to make decisions in general. The data was taken from Navy databases and from surveys of Navy experts. Such indeterminate subjective component makes the optimization of the constraint satisfaction a very sophisticated task. Single-Layer Perceptron with logistic regression, Multilayer Perceptron with different structures and algorithms and Support Vector Machine with Adatron algorithm are presented for achieving best performance. Multilayer Perceptron neural network and Support Vector Machine with Adatron algorithm produced highly accurate classification and encouraging prediction.
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Kelemen, A., Liang, Y., Kozma, R., Franklin, S. (2003). Optimizing Intelligent Agent’s Constraint Satisfaction with Neural Networks. In: Abraham, A., Jain, L.C., Kacprzyk, J. (eds) Recent Advances in Intelligent Paradigms and Applications. Studies in Fuzziness and Soft Computing, vol 113. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1770-6_12
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DOI: https://doi.org/10.1007/978-3-7908-1770-6_12
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