Abstract
The learning capabilities of CGPDN are explored to ‘recognize’ and ‘learn to play’ the arcade board game. The game of Checkers is selected as the arcade game since it was reconnoitred previously for learning by a number of AI algorithms. Like Wumpus world, checkers is also a grid based game however it is much more challenging and complicated compared to Wumpus world. Checkers is of great importance in the history of Artificial Intelligence and can be used as a test bed for evaluating the learning techniques (Dimand and Dimand 1996). The game of checkers is used here for demonstrating the capability of evolved networks to improve their ability to learn (level of play) by continuously playing against better opponents.
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Khan, G.M. (2018). Checkers. In: Evolution of Artificial Neural Development. Studies in Computational Intelligence, vol 725. Springer, Cham. https://doi.org/10.1007/978-3-319-67466-7_7
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DOI: https://doi.org/10.1007/978-3-319-67466-7_7
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Publisher Name: Springer, Cham
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Online ISBN: 978-3-319-67466-7
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