Discovering of Game AIs’ Characters Using a Neural Network based AI Imitator for AI Clustering

Abstract

In game AI research, most work aims at building a more powerful AI but few are reported on explaining AI’s behavior or revealing the characters of AI. Previous efforts in training a human-like AI via style learning implies that AI may behave with human characteristics. However, the early work treated human playstyle as a whole instead of identifying the difference among various AIs. In this paper, we focus on finding out manlike characters of individual AIs, and clustering AIs according to their characters. We propose a Neural Network based game AI imitator to imitate AIs’ behavior and find that some AIs are easier to imitate than others. Based on this observation we define the term imitability to describe the difficulty of imitation and cluster the AIs into two categories according to their imitability. Through statically analyzing, we find that AIs with lower imitability are generally farseeing with a global perspective while the other group are nearsighted and narrow-minded. The AIs hard to imitate also perform better when fighting with others. Upon the above semantic analysis of the clustering results, we conclude that the imitability can be used to identify AIs’ character.