This paper describes a method that predicts which trades players execute during a winlose game. Our method uses data collected from chat negotiations of the game The Settlers of Catan and exploits the conversation to construct dynamically a partial model of each player's preferences. This in turn yields equilibrium trading moves via principles from game theory. We compare our method against four baselines and show that tracking how preferences evolve through the dialogue and reasoning about equilibrium moves are both crucial to success.
REF investigated the prediction of player actions in the strategy board game The Settlers of Catan.
Grounding Strategic Conversation: Using Negotiation Dialogues to Predict Trades in a Win-Lose Game
EMNLP
Computer Science