How AI is Really Used in the Video Game Industry
The use of Artificial Intelligence (AI) in the video game industry has sparked numerous discussions online, ranging from optimistic predictions to speculative theories and misunderstandings. This article aims to provide a clear and concise overview of how AI is adopted in video games, covering intelligent characters, procedural generation, player modeling, texture upscaling, cheat detection, and natural language processing, among other applications.
Understanding AI in gaming
Defining artificial intelligence
Artificial Intelligence, a term coined in 1956, represents a collection of algorithms, philosophies, and techniques that enable computers to make intelligent decisions. AI is divided into two main categories: symbolic AI and machine learning.
- Symbolic AI involves embedding knowledge of problems into a series of states and actions, allowing systems to find sequences of actions to solve specific problems.
- Machine Learning includes algorithms that learn solutions by analyzing existing data, either unsupervised, by replicating phenomena, or through trial and error.
Machine Learning and Deep Learning
Machine learning, a subset of AI, has been around since 1959. It encompasses methods where algorithms learn from data and improve solutions iteratively. Deep learning, a further subset of machine learning, involves training large-scale artificial neural networks and has seen significant breakthroughs since the late 2000s. Generative AI, another advancement within deep learning, focuses on creating content such as stories, dialogue, audio assets, and art. Tools like OpenAI's ChatGPT exemplify generative AI, which is rapidly gaining traction in the gaming industry.
Recent Developments in AI
A few years ago, AI experienced a renaissance with the emergence of large language models (LLMs) like ChatGPT and image generators such as Stable Diffusion. Despite their flaws, these technologies have proven useful in a wide array of subjects. This raises the question: What about AI in video games? Astonishing graphics can only go so far if dialogues and AI behavior are poor enough to withdraw the player’s attention from the story or action.
Challenges with current AI and attempts to improve AI in games
NPC AI has consistently been the most buggy, boring, and immersion-breaking component in most AAA games released in the last decade. There are thousands of videos of games like Starfield, Cyberpunk, and Assassin’s Creed showcasing AI failures (some of which were fixed in updates). Often, AI either cheats to balance out the player's skill or performs actions with pixel-precision and perfect timing, leading to frustration rather than a challenge.
In the past, Bethesda's Radiant system aimed to make the game world feel more alive by having NPCs behave according to their needs and customs. While ambitious, it fell short, resulting in NPCs with set routines but little true dynamism. However, games like Rain World, F.E.A.R., Alien Isolation, and Stalker stand out for their impressive AI systems that adapt to the player's actions and the changing game world.
How is AI actually used in games?
1. Quality assurance and testing
AI significantly enhances quality assurance (QA) and testing by automating repetitive tasks and identifying bugs with greater efficiency. For example, Ubisoft uses AI-driven bots to test various scenarios in games like Assassin's Creed, where the AI can simulate thousands of player interactions to uncover bugs and glitches that human testers might miss. Similarly, Electronic Arts (EA) employs AI to test sports games like FIFA, where the AI can play countless matches to detect issues in gameplay mechanics and physics. This automation speeds up the testing process and allows for more comprehensive coverage, identifying edge cases and subtle bugs. Indie developers benefit as well, with AI tools enabling them to conduct thorough testing without large QA teams, ensuring their games are polished and bug-free before release.
2. Creating and improving graphics
AI boosts content creation in gaming by facilitating various aspects such as animation, texture enhancement, accessibility, and code generation. For example, in Hitman and The Last of Us Part Two, motion matching technology employs AI to generate realistic animations by selecting the best clips to match a character's movements. In Mass Effect: Legendary Edition, AI-driven texture upscaling transforms lower-resolution textures into high-quality visuals, reducing the need for manual labor. Furthermore, AI tools assist developers in generating code and design elements, thereby streamlining the development process. These advancements allow developers to focus more on creative tasks, improving the overall efficiency, quality, and accessibility of game production.
3. Generative AI and procedural generation
Procedural generation has been a staple in games for years, allowing developers to create expansive and varied game worlds with limited resources. Games like Minecraft, No Man's Sky, and Elite Dangerous use procedural algorithms to generate vast, unique environments. Generative AI takes this a step further, enabling the creation of entire game elements, from dialogues to quests, based on player interactions and preferences. This dynamic content generation can result in highly personalized gaming experiences, where the game adapts to the player's style and choices.
4. AI in NPCs
AI plays a crucial role in the behavior and interactions of non-player characters (NPCs) in video games. Advanced AI algorithms enable NPCs to exhibit more realistic and dynamic behaviors, responding to player actions in meaningful ways. For example, in The Elder Scrolls V: Skyrim's latest mod called Inworld Skyrim - AI NPCS, AI-driven NPCs can engage in complex routines, interact with the player based on previous encounters, and adapt their behavior to the evolving game world. Games like Red Dead Redemption 2 take this further by creating NPCs with rich backstories and daily routines that make the game world feel alive and immersive. These advancements in AI not only enhance the realism of the game world but also create more engaging and unpredictable gameplay experiences.
5. Combating cheating and toxicity
Machine learning algorithms play a crucial role in identifying and mitigating cheating and toxic behavior in online games. For example, Valve utilizes deep learning techniques in Counter-Strike: Global Offensive to detect and ban cheaters. The AI analyzes gameplay data to identify abnormal patterns, such as improbably accurate aiming or impossible movements, which are indicative of cheating software. Additionally, these algorithms can monitor player communications to detect and address toxic behavior, such as harassment or hate speech. This proactive approach helps maintain a fair and enjoyable gaming environment by swiftly dealing with bad actors, thereby improving the overall player experience and community health.
6. Player modeling and analytics
Player modeling and analytics use AI to analyze player behavior and preferences within a game, providing developers with insights into how players interact with the game. For instance, in Tomb Raider: Underworld, developers monitored player navigation, puzzle-solving, and interaction patterns to make data-driven improvements. This analysis helps developers optimize game design by adjusting difficulty, enhancing popular elements, and creating more engaging experiences, leading to increased player satisfaction and retention.
Future directions for AI in gaming
Potential applications of AI in gaming
As AI technology continues to evolve, several potential applications could revolutionize gaming:
- Dynamic NPC Agendas: NPCs and factions could have evolving agendas based on their backstories and the game's world state. For example, an NPC like Tim, a farmer who lost his family in war, might decide to travel, join the guard, or start a cult based on his personality traits and experiences.
- Enhanced Interactions: AI could improve NPC interactions, providing more genuine reactions and varied dialogue as featured in NVIDIA ACE. For example, an NPC might react uniquely to repeated player actions or be persuaded to join the player's cause through dialogue.
- World State Regulation: The game world could balance itself, promoting peaceful behaviors in chaotic times or inciting conflicts when the game becomes too calm, ensuring a dynamic and engaging experience.
- Procedural Content Generation: Future AI advancements could enable the procedural generation of maps and game content, creating immersive and lore-consistent environments on the fly. Unreal Engine 5 already supports some procedural content generation functions. Additionally, generative AI has the potential to create entire games from scratch. Google's recent Genie project demonstrates how AI can generate whole games using computer vision and language models. By analyzing millions of short clips from platformer games, Genie can predict future frames and player actions, effectively generating playable games based on simple descriptions.
Technical challenges and solutions
While the potential of AI in gaming is vast, there are significant technical challenges to overcome. Running AI models, especially LLMs, requires substantial computational power. This often necessitates powerful GPUs, which can be expensive and resource-intensive. Developers are exploring various solutions to address these challenges, including cloud computing and decentralized approaches. Cloud-based AI services can offload the computational burden from the player's device, providing high-quality AI interactions without compromising performance. Decentralized computing, where multiple players' devices share the load, is another innovative approach that could make advanced AI more accessible.
Ethical considerations
As AI continues to integrate into gaming, ethical considerations become increasingly important. Issues such as data privacy, AI bias, and the potential for addictive gameplay must be addressed. Developers need to ensure that AI enhances the gaming experience without compromising player welfare. Looking forward, the future of AI in gaming is promising. Continued advancements in machine learning, natural language processing, and generative AI will lead to more immersive, dynamic, and personalized gaming experiences. The gaming industry is on the cusp of a new era, where AI plays a central role in shaping the future of interactive entertainment.
Conclusion
Artificial Intelligence is transforming the video game industry by enhancing player experiences, improving game production processes, and enabling innovative content creation. As AI technology continues to evolve, it will undoubtedly play an increasingly pivotal role in shaping the future of gaming.
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Patrick Yu is a Senior Project Manager at Level Interactive and has 8 years of experience writing business, legal, lifestyle, gaming, and technology articles. He is a significant contributor to Acer Corner and is currently based in Taipei, Taiwan.
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