Strategic conversational agents often need to trade resources with their opponent conversants -- and trading strategically can lead to better results. While rule-based or supervised agents can be used for such a purpose, here we explore a learning approach based on automatically labelled examples from human players for automatic trading in the game of Settlers of Catan. Our experiments are based on data collected from human players trading in text-based natural language. We compare the performance of Bayes Nets, Conditional Random Fields, and Random Forests on the task of ranking trading offers, trained from both manually labelled and automatically labelled data. Our experimental results show that our best agent trained on automatic labels outperformed its counterpart trained on manual labels (with moderate annotator agreement) in terms of (a) predicting human trading negotiations better, and (b) winning more games.
['Heriberto Cuayáhuitl', 'Simon Keizer', 'Oliver Lemon']
Learning Trading Negotiations Using Manually and Automatically Labelled Data