LS-VisionDraughts: improving the performance of an agent for checkers by integrating computational intelligence, reinforcement learning and a powerful search method

@article{Neto2014LSVisionDraughtsIT,
  title={LS-VisionDraughts: improving the performance of an agent for checkers by integrating computational intelligence, reinforcement learning and a powerful search method},
  author={Henrique Castro Neto and Rita Maria Silva Julia and Gutierrez Soares Caexeta and Ayres Roberto Ara{\'u}jo Barcelos},
  journal={Applied Intelligence},
  year={2014},
  volume={41},
  pages={525 - 550}
}
This paper presents LS-VisionDraughts: an efficient unsupervised evolutionary learning system for Checkers whose contribution is to automate the process of selecting an appropriate representation for the board states – by means of Evolutionary Computation – keeping a deep look-ahead (search depth) at the moment of choosing an adequate move. It corresponds to a player Multi Layer Perceptron Neural Network whose weights are updated through an evaluation function that is automatically adjusted by… 
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14 Citations

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  • 2016
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