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 da 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… CONTINUE READING

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References

Publications referenced by this paper.
SHOWING 1-10 OF 57 REFERENCES

Checkerboard program - version 1.72

MC Fierz
  • Technical report. Available in http://www.fierz.ch/checkerboard.php
  • 2012
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Artificial Intelligence for Games

  • The Morgan Kaufmann series in interactive 3D technology
  • 2006
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Artificial intelligence - a modern approach, 2nd Edition

  • Prentice Hall series in artificial intelligence
  • 2003
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Neural Networks: A Comprehensive Foundation

VIEW 6 EXCERPTS
HIGHLY INFLUENTIAL

Temporal Difference Learning and TD-Gammon

  • ICGA Journal
  • 1995
VIEW 8 EXCERPTS
HIGHLY INFLUENTIAL