一句话：提出通过智能体间差分比较来解释游戏智能体行为，并应用于商业GaaS原型验证其可提取具体行为差异。
Game-as-a-Service（GaaS）频繁更新，测试时间有限，自动游戏智能体（尤其深度强化学习智能体）可大幅增加试玩，但其黑箱特性导致难以理解具体行为；现有解释技术常只给出不具区分度的共性行为。本文提出用智能体之间的差分比较来解释游戏智能体行为：通过对比不同学习算法训练出的智能体、人类与自动智能体、测试智能体与用户等，抽取并突出行为差异。作者将方法应用于一个商业GaaS原型，结果确认该技术能够提取智能体间的具体差异，帮助理解其行为。
The difficulty in exploring the game balance has been increasing, especially in Game-as-a-Service (GaaS) with updates in every few weeks, and due to the complexity in game design and business models. In the limited time available for testing, using automated game agents enables much more test plays than using human test players does, and it has been accelerated by the recent progress of deep reinforcement learning. However, understanding specific behaviours of each agent is hard due to their “black-box” nature. In this paper, we propose a method for explaining the behaviour of game agents using differential comparison between agents. This comparison approach is motivated by our experience with existing explanation techniques that often extracted uninteresting, common aspects of the behaviour. In addition, there are large potentials for the application of the comparison: between agents with different learning algorithms, between human agents and automated agents, and between test agents and users. We applied our technique to a prototype of a commercial GaaS and confirmed our technique can extract specific differences between agents.
@inproceedings{paperbot7149,
title = {Explaining the Behaviour of Game Agents Using Differential Comparison},
author = {Ezequiel Castellano and Xiao-Yi Zhang and Paolo Arcaini and Toru Takisaka and Fuyuki Ishikawa and Nozomu Ikehata and Kosuke Iwakura},
booktitle = {ASE},
year = {2022},
doi = {10.1145/3551349.3560503}
}