Playtesting: What is Beyond Personas

Download

index.pdf

Date

2021-07-01

Author

Ariyurek, Sinan
Sürer, Elif
Betin Can, Aysu

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

215
views

170
downloads

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.

Subject Keywords

Reinforcement Learning, Player modeling, Automated playtesting, Play persona

URI

https://hdl.handle.net/11511/91414

Collections

Graduate School of Informatics, Article

Suggestions

OpenMETU
Core

Playtesting: What is Beyond Personas Ariyurek, Sinan; Sürer, Elif; Betin Can, Aysu (2022-01-01) We 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 trai...
AUTOMATED VIDEO GAME TESTING USING REINFORCEMENT LEARNING AGENTS Arıyürek, Sinan; Sürer, Elif; Betin Can, Aysu; Department of Bioinformatics (2022-9-21) In this thesis, several methodologies are introduced to automate and improve video game playtesting. These methods are based on Reinforcement Learning (RL) agents. First, synthetic and human-like tester agents are proposed to automate video game testing. The synthetic agent uses test goals generated from game scenarios, and the human-like agent uses test goals extracted from tester trajectories. Tester agents are derived from Sarsa and Monte Carlo Tree Search (MCTS) but focus on finding defects, while tradi...
An Ontology – based approach to design a serious game for teacher education Dehghan, Sanam; İşler, Veysi; Department of Modeling and Simulation (2015) Increasing popularity of serious games draws many game designers’ attention and encourages developers to involve with the field of education. In this respect, an effective serious game design becomes prominent to attain a place in the domain. That is, a successful serious game design should attach importance to educational constituents as well as game design components. On the other hand, merely adding a limited number of game elements into an educational simulation could not lead to a successful serious ga...
Visual Structuring for Generative Design Search Spaces Abbas, Günsu Merin; Gürsel Dino, İpek (2016-06-17) With generative design strategies, the act of design, the problem-solving process, and the interaction, understanding and representation of design artefacts have changed. As such strategies entail a large number of possible design solutions, they also expand the design search space immensely. In addition, due to the automated design generation process, the interaction of the designer through the design process has decreased. This research problematizes two major points as (1) the broad design spaces of gene...
Level generation using genetic algorithms and difficulty testing using reinforcement learning in match-3 game Dukkancı, Samet Alp; Alpaslan, Ferda Nur; Department of Computer Engineering (2021-9) The gaming industry is an enormous one that includes game development, art, and marketing. It has grown faster in recent years, especially in the mobile gaming area. Competition in mobile gaming increased in such a way that it brings us some challenges like quick prototyping, automation, minimum viable products, and so on. Level generation is the most important issue in the development period because unique and well-adjusted difficulty for a level is generally tested by humans many times for one level to as...

Citation Formats

S. Ariyurek, E. Sürer, and A. Betin Can, “Playtesting: What is Beyond Personas,” 2021, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/91414.