An Application of Neural Networks to a Non-Deterministic Game of Imperfect Information - Lydeen, Nicholas; Ahrendt, Chris R. (My University, 2017)
Current research into game-playing neural networks emphasizes deterministic games of per- fect information and training with “expert knowledge” such as known successful strategies. As an example, Google’s AlphaGo, a neural network agent designed to play the game Go, was trained with over 30 million different board positions drawn from approximately 160,000 different games. This expert knowledge gives AlphaGo a solid baseline for future play. We studied the application of neural networks to the game Lost Cities, a non-deterministic game of imperfect information, without providing such expert knowledge, and designed several neural network agents and implemented an agent using Monte Carlo tree search for comparison.
Color poster with text, graphs, and images.
Keywords: Posters; Computer games; Strategy games; Neural networks; AlphaGo