Online games are dynamic environments where players interact with each other, which offers a rich setting for understanding how players negotiate their way through the game to an ultimate victory. This work studies online player interactions during the turn-based strategy game, Diplomacy. We annotated a dataset of over 10,000 chat messages for different negotiation strategies and empirically examined their importance in predicting long- and short-term game outcomes. Although negotiation strategies can be predicted reasonably accurately through the linguistic modeling of the chat messages, more is needed for predicting short-term outcomes such as trustworthiness. On the other hand, they are essential in graph-aware reinforcement learning approaches to predict long-term outcomes, such as a player's success, based on their prior negotiation history. We close with a discussion of the implications and impact of our work. The dataset is available at https://github.com/kj2013/claff-diplomacy.

翻译：在线游戏是玩家相互互动的动态环境，为理解玩家如何在游戏中通过谈判走向最终胜利提供了丰富的研究场景。本研究聚焦于回合制策略游戏《外交》中的在线玩家互动。我们标注了一个包含超过1万条聊天消息的数据集，涵盖不同谈判策略，并通过实证检验了这些策略在预测游戏短期与长期结果中的重要性。尽管通过聊天消息的语言建模可以较为准确地预测谈判策略，但对于预测信任度等短期结果仍显不足。另一方面，在图感知强化学习（graph-aware reinforcement learning）方法中，这些策略对于基于玩家以往谈判历史预测长期结果（如玩家最终的成功）至关重要。我们最后讨论了本研究的意义与影响。数据集开放于 https://github.com/kj2013/claff-diplomacy。