Playtesting: What is Beyond Personas


Ariyurek S., Sürer E., Betin Can A.

IEEE Transactions on Games, 2022 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Publication Date: 2022
  • Doi Number: 10.1109/tg.2022.3165882
  • Journal Name: IEEE Transactions on Games
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Keywords: Automated Playtesting, Games, Measurement, Neural networks, Optimization, Play Persona, Player Modeling, Q-learning, Reinforcement Learning, Training, Trajectory
  • Middle East Technical University Affiliated: Yes

Abstract

IEEEWe present two approaches to improve automated playtesting. First, we propose developing persona, which allows a persona to progress to different goals. In contrast, the procedural persona is fixed to a single goal. Second, a human playtester knows which paths she has tested before, and during the consequent tests, she may test different paths. However, Reinforcement Learning (RL) agents disregard these previous paths. We propose a novel methodology that we refer to as Alternative Path Finder (APF). We train APF with previous paths and employ APF during the training of a RL agent. APF modulates the reward structure of the environment while preserving the agent's goal. When evaluated, the agent generates a different trajectory that achieves the same goal. We use the General Video Game Artificial Intelligence and VizDoom frameworks to test our proposed methodologies. We use Proximal Policy Optimization RL agent during experiments. First, we compare the playtest data generated by developing and procedural persona. Our experiments show that developing persona provides better insight into the game and how different players would play. Second, we present the alternative paths found using APF and argue why traditional RL agents cannot learn those paths.