论文标题
使用心理理论改善多代理合作
Improving Multi-Agent Cooperation using Theory of Mind
论文作者
论文摘要
人工智能的最新进展产生了可以在GO，Starcraft和Dota2等游戏中击败人类世界冠军的代理商。但是，这些模型中的大多数似乎并没有以人类的方式发挥作用：人们从其行为中推断出他人的意图，并在策略和策略中使用这些推论。在这里，使用贝叶斯的心理理论（TOM）方法，我们研究了他人意图的明确表示有多少改善了合作游戏中的表现。在合作游戏中，我们比较了人类与具有和没有汤姆的最佳计划代理的表现，在该游戏中，玩家必须灵活合作才能实现共同的目标。我们发现，与汤姆代理商的团队与所有类型的合作伙伴合作时：Non-Tom，Tom和Human Players合作时，汤姆的好处增加了汤姆·特工的好处。这些发现对设计更好的合作社具有影响。
Recent advances in Artificial Intelligence have produced agents that can beat human world champions at games like Go, Starcraft, and Dota2. However, most of these models do not seem to play in a human-like manner: People infer others' intentions from their behaviour, and use these inferences in scheming and strategizing. Here, using a Bayesian Theory of Mind (ToM) approach, we investigated how much an explicit representation of others' intentions improves performance in a cooperative game. We compared the performance of humans playing with optimal-planning agents with and without ToM, in a cooperative game where players have to flexibly cooperate to achieve joint goals. We find that teams with ToM agents significantly outperform non-ToM agents when collaborating with all types of partners: non-ToM, ToM, as well as human players, and that the benefit of ToM increases the more ToM agents there are. These findings have implications for designing better cooperative agents.
