DeepAI AI Chat
Log In Sign Up

Playtesting: What is Beyond Personas

by   Sinan Ariyurek, et al.
Middle East Technical University

Playtesting is an essential step in the game design process. Game designers use the feedback from playtests to refine their design. Game designers may employ procedural personas to automate the playtesting process. In this paper, we present two approaches to improve automated playtesting. First, we propose a goal-based persona model, which we call developing persona – developing persona proposes a dynamic persona model, whereas the current persona models are static. Game designers can use the developing persona to model the changes that a player undergoes while playing a game. Additionally, a human playtester knows which paths she has tested before, and during the consequent tests, she may test different paths. However, RL agents disregard the previously generated trajectories. We propose a novel methodology that helps Reinforcement Learning (RL) agents to generate distinct trajectories than the previous trajectories. We refer to this methodology as Alternative Path Finder (APF). We present a generic APF framework that can be applied to all RL agents. APF is trained with the previous trajectories, and APF distinguishes the novel states from similar states. We use the General Video Game Artificial Intelligence (GVG-AI) and VizDoom frameworks to test our proposed methodologies. We use Proximal Policy Optimization (PPO) RL agent during experiments. First, we show that the playtest data generated by the developing persona cannot be generated using the procedural personas. Second, we present the alternative paths found using APF. We show that the APF penalizes the previous paths and rewards the distinct paths.


page 1

page 3

page 6

page 7

page 9


Automated Video Game Testing Using Synthetic and Human-Like Agents

In this paper, we present a new methodology that employs tester agents t...

Human-level performance in first-person multiplayer games with population-based deep reinforcement learning

Recent progress in artificial intelligence through reinforcement learnin...

Reinforcement Learning Agents for Ubisoft's Roller Champions

In recent years, Reinforcement Learning (RL) has seen increasing popular...

Deep Hedging, Generative Adversarial Networks, and Beyond

This paper introduces a potential application of deep learning and artif...

Insights From the NeurIPS 2021 NetHack Challenge

In this report, we summarize the takeaways from the first NeurIPS 2021 N...

Can Agents Run Relay Race with Strangers? Generalization of RL to Out-of-Distribution Trajectories

In this paper, we define, evaluate, and improve the “relay-generalizatio...

Exploring Gameplay With AI Agents

The process of playtesting a game is subjective, expensive and incomplet...