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.

本论文首次引入了 Feint 在多人游戏中的形式化、实现和定量评估，通过基于非传递性主动马尔可夫游戏模型的形式化，结合最新的多智能体建模进展（即多智能体强化学习），我们考虑了 Feint 在多人游戏中的实际实施细节，并定量检验了我们设计的有效性，结果表明我们的 Feint 设计可以显著提高游戏的奖励收益，增加多人游戏的多样性，并且在时间消耗方面几乎没有额外开销。结论是我们的 Feint 设计是有效和实用的，可以使多人游戏更有趣。