Adapting BERT for 'Apples to Apples' Gameplay

"Apples to Apples" is a word association game where players match red noun cards with a given green adjective card in the hopes of winning the round through the selection of their card by the round judge. There are many possible selection criteria for determining which red card to play in a round. Combinations may focus solely on the direct association between a red and green card, on the humour elicited by a card pair, or through a wide variety of other selection criteria. The game's difficulty comes from the anticipation and matching of the card played by a player to the criterion of the current round judge. Our aim was to create an agent capable of playing "Apples to Apples," able to discover the current judge criterion and play the card in hand that best matches that criterion. To this end, we decided to explore the use of BERT pretrained models and evaluate their viability in word game agents. In this study, we leveraged fine tuned BERT models and developed a Naive Bayes classifier to simulate and predict judge personalities in "Apples to Apples." Beyond these main contributions, this work also involved significant efforts in creating supporting tools for both training and testing processes. Specifically, we manually annotated nearly 1000 card pairs using a custom annotation program to reduce human error and fatigue and developed an environment testing tool to ensure platform consistency during training and testing. These contributions highlight our focus on both data quality and system robustness.