Turing Test Framework for Cooperative Games

Abstract

Recently, several attempts have been made to train cooperative artificial intelligence (AI). From training superhuman-level agents to human-like agents, the purpose of an AI results in differences in the behavior policy. Indeed, training a human-like agent could enhance the experience of multiplayer game players. However, training human-like agents is challenging and there is little existing work concerning benchmarking cooperative agents with actual humans. As an initial step to address this problem, we suggest a software program and an experimental procedure to conduct Turing tests in multiplayer games. Our contribution will help current multiagent studies benchmark the human-likeness of the agents and investigate their characteristics.