Preference-conditioned Pixel-based AI Agent For Game Testing Article Swipe

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Sherif Abdelfattah , A. J. Brown , Pushi Zhang ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2308.09289

The game industry is challenged to cope with increasing growth in demand and game complexity while maintaining acceptable quality standards for released games. Classic approaches solely depending on human efforts for quality assurance and game testing do not scale effectively in terms of time and cost. Game-testing AI agents that learn by interaction with the environment have the potential to mitigate these challenges with good scalability properties on time and costs. However, most recent work in this direction depends on game state information for the agent's state representation, which limits generalization across different game scenarios. Moreover, game test engineers usually prefer exploring a game in a specific style, such as exploring the golden path. However, current game testing AI agents do not provide an explicit way to satisfy such a preference. This paper addresses these limitations by proposing an agent design that mainly depends on pixel-based state observations while exploring the environment conditioned on a user's preference specified by demonstration trajectories. In addition, we propose an imitation learning method that couples self-supervised and supervised learning objectives to enhance the quality of imitation behaviors. Our agent significantly outperforms state-of-the-art pixel-based game testing agents over exploration coverage and test execution quality when evaluated on a complex open-world environment resembling many aspects of real AAA games.

Related Topics

Computer Science

The Imitation Game

Game Design

Artificial Intelligence

Video Game

Generalization

Human–Computer Interaction

Mathematical Analysis

Epistemology

Philosophy

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Computer science Imitation Preference Game design Scalability Quality (philosophy) Artificial intelligence Video game Generalization Game mechanics Human–computer interaction Multimedia Microeconomics Economics Mathematics Social psychology Psychology Database Mathematical analysis Epistemology Philosophy

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2308.09289
PDF: https://arxiv.org/pdf/2308.09289
OA Status: green
Related Works: 10
OpenAlex ID: https://openalex.org/W4386043464

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DOI: https://doi.org/10.48550/arxiv.2308.09289

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Title: Preference-conditioned Pixel-based AI Agent For Game Testing

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Type: preprint

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Language: en

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Publication year: 2023

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Publication date: 2023-08-18

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Authors: Sherif Abdelfattah, A. J. Brown, Pushi Zhang

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Landing page: https://arxiv.org/abs/2308.09289

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PDF URL: https://arxiv.org/pdf/2308.09289

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Concepts: Computer science, Imitation, Preference, Game design, Scalability, Quality (philosophy), Artificial intelligence, Video game, Generalization, Game mechanics, Human–computer interaction, Multimedia, Microeconomics, Economics, Mathematics, Social psychology, Psychology, Database, Mathematical analysis, Epistemology, Philosophy

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Related works (count): 10

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abstract_inverted_index.Game-testing	46
abstract_inverted_index.observations	147
abstract_inverted_index.demonstration	159
abstract_inverted_index.significantly	185
abstract_inverted_index.trajectories.	160
abstract_inverted_index.generalization	90
abstract_inverted_index.representation,	87
abstract_inverted_index.self-supervised	171
abstract_inverted_index.state-of-the-art	187
cited_by_percentile_year
countries_distinct_count	0
institutions_distinct_count	3
sustainable_development_goals[0].id	https://metadata.un.org/sdg/9
sustainable_development_goals[0].score	0.4399999976158142
sustainable_development_goals[0].display_name	Industry, innovation and infrastructure
citation_normalized_percentile