Learning high quality decisions with neural networks in "conscious" software agents

  • Kelemen A
  • Liang Y
  • Franklin S
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Abstract

Finding suitable jobs for US Navy sailors periodically 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 statistical criteria to aid IDA's constraint satisfaction, which is also capable of learning high quality decision making over time. Multilayer Perceptron (MLP) with different structures and algorithms, Feedforward Neural Network (FFNN) with logistic regression and Support Vector Machine (SVM) with Radial Basis Function (RBF) as network structure and Adatron learning algorithm are presented for comparative analysis. Discussion of Operations Research and standard optimization techniques is also provided. The subjective indeterminate nature of the detailer decisions make the optimization problem nonstandard. Multilayer Perceptron neural network with structural learning and Support Vector Machine produced highly accurate classification and encouraging prediction.

Author-supplied keywords

  • Decision making
  • Multilayer perceptron
  • Optimization
  • Structural learning
  • Support vector machine

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  • SGR: 24344433549
  • PUI: 41250166
  • ISSN: 11092777
  • SCOPUS: 2-s2.0-24344433549

Authors

  • Arpad Kelemen

  • Yulan Liang

  • Stan Franklin

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