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.

本文探讨了用机器的 “心智理论” 来针对不同人格类型的对手去自适应调整对话体系的高层策略，我们通过 CraigslistBargain 数据集的测试表明，利用心智推理方法相比于基线方法在混合对手的情况下显示出更高的对话一致性；同时我们也发现，我们的模型表现出了与不同类型对手完全不同的谈判行为。

通过人格建模提升对话系统的谈判能力

Improving Dialog Systems for Negotiation with Personality Modeling

A long-term goal of machine learning research is to build an intelligent
dialog agent. Most research in natural language understanding has focused on
learning from fixed training sets of labeled data, with supervision either at
the word level (tagging, parsing tasks) or sentence level (question answering,
machine translation). This kind of supervision is not realistic of how humans
learn, where language is both learned by, and used for, communication. In this
work, we study dialog-based language learning, where supervision is given
naturally and implicitly in the response of the dialog partner during the
conversation. We study this setup in two domains: the bAbI dataset of (Weston
et al., 2015) and large-scale question answering from (Dodge et al., 2015). We
evaluate a set of baseline learning strategies on these tasks, and show that a
novel model incorporating predictive lookahead is a promising approach for
learning from a teacher's response. In particular, a surprising result is that
it can learn to answer questions correctly without any reward-based supervision
at all.

研究人员在机器学习和自然语言理解方面的不懈探索中探讨了基于对话的语言学习，发现预测前瞻是一种有前途的学习策略，并表明它可以在没有奖励监督的情况下正确地回答问题。