  Evolutionary game theory has been a successful tool to combine classical game
theory with learning-dynamical descriptions in multiagent systems. Provided
some symmetric structures of interacting players, many studies have been
focused on using a simplified heuristic payoff table as input to analyse the
dynamics of interactions. Nevertheless, even for the state-of-the-art method,
there are two limits. First, there is inaccuracy when analysing the simplified
payoff table. Second, no existing work is able to deal with 2-population
multiplayer asymmetric games. In this paper, we fill the gap between heuristic
payoff table and dynamic analysis without any inaccuracy. In addition, we
propose a general framework for $m$ versus $n$ 2-population multiplayer
asymmetric games. Then, we compare our method with the state-of-the-art in some
classic games. Finally, to illustrate our method, we perform empirical
game-theoretical analysis on Wolfpack as well as StarCraft II, both of which
involve complex multiagent interactions.
