This paper introduces the first formalization, implementation and quantitative evaluation of Feint in Multi-Player Games. Our work first formalizes Feint from the perspective of Multi-Player Games, in terms of the temporal, spatial, and their collective impacts. The formalization is built upon Non-transitive Active Markov Game Model, where Feint can have a considerable amount of impacts. Then, our work considers practical implementation details of Feint in Multi-Player Games, under the state-of-the-art progress of multi-agent modeling to date (namely Multi-Agent Reinforcement Learning). Finally, our work quantitatively examines the effectiveness of our design, and the results show that our design of Feint can (1) greatly improve the reward gains from the game; (2) significantly improve the diversity of Multi-Player Games; and (3) only incur negligible overheads in terms of time consumption. We conclude that our design of Feint is effective and practical, to make Multi-Player Games more interesting.

翻译：本文首次提出了多玩家游戏中佯攻的形式化定义、实现方法及定量评估。我们的工作首先从多玩家游戏视角出发，基于时间、空间及其综合影响对佯攻进行形式化描述。该形式化建立在非传递性主动马尔科夫博弈模型之上，使得佯攻能够产生显著影响。随后，本研究结合当前多智能体建模的最新进展（即多智能体强化学习），探讨了多玩家游戏中佯攻的实际实现细节。最后，我们通过定量实验检验了设计有效性，结果表明：所提出的佯攻设计能够（1）大幅提升游戏收益；（2）显著增强多玩家游戏的多样性；（3）仅引入可忽略的时间开销。我们得出结论：该佯攻设计在提升多玩家游戏趣味性方面兼具有效性与实用性。