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AI in game development: An efficient replacement for play-testers

Artificial intelligence in game development can fine-tune and speed up the process of producing better games.

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Quantumrun Foresight
July 12, 2022

Insight summary

As multiplayer internet games gain massive popularity, game developers are turning to artificial intelligence (AI) and machine learning (ML) to create more engaging, error-free games faster. These technologies are transforming game development by enabling rapid testing and refinement, reducing the need for extensive human playtesting, and allowing for more personalized and diverse game experiences. This shift can also influence other sectors, from education and marketing to environmental sustainability and cultural understanding.

AI in game development context

Internet multiplayer games have grown in popularity since the mid-2000s, enthralling millions of gamers worldwide. However, this success puts pressure on game creators to churn out increasingly well-crafted, bug-free, structured video games. Games can quickly lose popularity if fans and users feel the game is not challenging enough, is not repeatedly playable, or has flaws in its design.

Artificial intelligence and ML are being increasingly integrated into game development, where game designers are replacing human play-testers with ML models to fine-tune the development process. It typically takes months of playtesting to detect inequities in a newly prototyped game during the game development process. When an error or imbalance is identified, it can take days to alleviate the problem.

A recent strategy to combat this issue sees ML tools deployed to change gameplay equilibrium, with ML using its earning algorithms to act as play-testers. An example of a game where this was trialed was the digital card game prototype Chimera, which has been previously used as a testing ground for ML-generated art. The ML-based testing process enables game designers to make a game more interesting, equitable, and consistent with its original concept. The technique also takes less time by running millions of simulation experiments using trained ML agents to conduct research.

Disruptive impact

By mentoring new players and devising innovative playing strategies, ML agents can enhance the gaming experience. Their utilization in game testing is also noteworthy; if successful, developers might increasingly rely on ML for both game creation and workload reduction. This shift can particularly benefit new developers, as ML tools often don't require deep coding knowledge, allowing them to engage in game development without the barrier of complex scripting. This ease of access could democratize game design, opening doors for a wider range of creators to develop games across various genres, including educational, scientific, and entertainment.

The integration of AI in game development is expected to streamline the testing and refinement process, allowing developers to rapidly implement improvements. Advanced AI systems, using predictive models, could potentially design entire games based on limited inputs like keyframes and consumer data. This ability to analyze and apply user preferences and trends could lead to the creation of games that are highly tailored to player interests and experiences. Moreover, this predictive capacity of AI might enable developers to anticipate market trends and consumer needs, leading to more successful game launches.

Looking forward, the scope of AI in game development could expand to encompass more creative aspects. AI systems might eventually be capable of generating in-game graphics, sound, and even narratives, offering a level of automation that could transform the industry. Such advancements could result in a surge of innovative and complex games, developed more efficiently than ever before. This evolution may also lead to new forms of interactive storytelling and immersive experiences, as AI-generated content could introduce elements that are currently unfeasible for human developers alone.

Implications of AI testing in game development

Wider implications of using AI testing and analysis systems in game development include:

Companies rapidly developing and releasing more games annually, leading to increased profits and a more dynamic gaming market.

A decline in games with poor reception due to enhanced testing by AI systems, resulting in fewer coding errors and higher overall game quality.

Longer average game durations across various genres, as reduced production costs enable more extensive storylines and expansive open-world environments.

Brands and marketers increasingly embracing game development for promotional purposes, as lower costs make branded games a more viable marketing strategy.

Media companies reallocating a significant portion of their film and television budgets to video game production, recognizing the growing appeal of interactive entertainment.

AI-driven game development creating new job opportunities in creative design and data analysis, while reducing traditional coding roles.

Governments formulating new regulations for AI in game development to ensure ethical use of data and to safeguard against potential misuse.

Educational institutions integrating AI-developed games into their curricula, providing more interactive and personalized learning experiences.

Environmental benefits from reduced physical game production, as AI accelerates the shift towards digital distribution.

A cultural shift as AI-generated games offer diverse narratives and experiences, potentially leading to a broader understanding and appreciation of different cultures and perspectives.