  In this paper, we explore the ability to model and infer personality types of
opponents, predict their responses, and use this information to adapt a dialog
agent's high-level strategy in negotiation tasks. Inspired by the idea of
incorporating a theory of mind (ToM) into machines, we introduce a
probabilistic formulation to encapsulate the opponent's personality type during
both learning and inference. We test our approach on the CraigslistBargain
dataset and show that our method using ToM inference achieves a 20% higher
dialog agreement rate compared to baselines on a mixed population of opponents.
We also find that our model displays diverse negotiation behavior with
different types of opponents.
