Generating Strategic Dialogue for Negotiation with Theory of Mind

We propose a framework to integrate the concept of Theory of Mind (ToM) into generating utterances for task-oriented dialogue. Our approach explores the ability to model and infer personality types of opponents, predicts their responses, and uses this information to adapt the agent's high-level strategy in negotiation tasks. We introduce a probabilistic formulation for the first-order theory of mind and test our approach on the CraigslistBargain dataset. Experiments show that our method using ToM inference achieves a 40\% higher dialogue agreement rate compared to baselines on a mixed population of opponents. We also show that our model displays diverse negotiation behavior with different types of opponents.