  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.
