Playtesting: What is Beyond Personas
  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.
