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Overview:
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Multiplayer boardgame adaptations require competitive AI opponents for solo play. But what's the best approach to creating these opponents? Temple Gates has scripted a heuristic AI for 'Ascension' and integrated a neural network for 'Race for the Galaxy'. Since then, each new game has used new techniques to improve AI competitiveness. Deckbuilder 'Shards of Infinity's' AI identifies symmetries, such as symmetries between players or cards, to supercharge the speed of training. 'Roll for the Galaxy's' AI is designed with holes in the inputs to speed training without creating strategic blindspots. 'Dominion' introduces an embedding layer, so the neural network can learn not only the cards themselves, but the value of their constituent features. This helps handle the highly variant game state where one of a quadrillion of permutations of cards may be in play. Understanding what techniques are available in ml and which best apply to different game scenarios is a shortcut to getting the most of a nn AI.
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