Evolutionary Game-Theoretical Analysis for General Multiplayer Asymmetric Games

TL;DR

This paper introduces a precise, general framework for analyzing complex multiplayer asymmetric games using evolutionary game theory, overcoming previous limitations of simplified payoff tables and extending analysis to 2-population scenarios.

Contribution

It presents a novel, accurate method for dynamic analysis of asymmetric multiplayer games and extends evolutionary game theory to 2-population settings.

Findings

Accurate analysis of asymmetric multiplayer games achieved.

Framework successfully applied to Wolfpack and StarCraft II.

Outperforms existing methods in classic game comparisons.

Abstract

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.