Optimizing decision making with neural networks in software agents

Finding suitable jobs for US Navy sailors from time to time is an important and ever-changing process. An Intelligent Distribution Agent (IDA) and particularly its constraint satisfaction module take up the challenge to automate the process. The constraint satisfaction module's main task is to provide the bulk of the decision making process in assigning sailors to new jobs in order to maximize Navy and sailor "happiness". We propose Multilayer Perceptron neural network with structural learning in combination with several statistical criteria to aid IDA's constraint satisfaction module, which is also capable of learning high quality decision making over time. Data were taken from Navy databases and from surveys of Navy experts. Results show highly accurate classification and encouraging prediction.

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