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One of the most exciting and promising areas for video game AI is the ability to create generative characters and worlds, which can grow and evolve with the player in real-time. This could enable games where the player and the AI storyteller co-author a unique narrative, resulting in a never-before-seen game experience for every playthrough. This kind of generative game AI is already being used by startups such as Inworld AI, which has raised over $500 million in funding and is currently the best-funded gaming startup.

Another exciting area of video game AI is combat AI, with many developers relying on advancements in AI and machine learning to create more realistic enemies that are both challenging and enjoyable to fight. One of the key advances here has been the concept of survival instinct, where the AI can look for and react to markers in a game that indicate if it is being hunted. For example, if the AI notices that it has very little health left, then it can look for cover to protect itself until it can reload. This is a feature that was originally pioneered in Monolith’s F.E.A.R. series in 2005.

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Generative AI, which is used to create art and 3D models, raises privacy concerns. Many tools are trained on scraped images and text, which some experts argue amounts to mass copyright infringement. To reduce these issues, developers are designing ethical systems that only work on authorised data.

For example, Microsoft’s Muse is a generative AI that helps designers test gameplay without the need for human testers. It saves development time and can generate more granular data about what is working and what is not. This provides more accurate insights compared to human feedback, which may be influenced by factors like personal bias and emotion.

Can AI Learn to Play Like a Human?

Other uses of AI in gaming include improving NPC behavior, enhancing game balance and combat, and providing limitless content through procedural generation. AI can help improve realism in game characters by using pathfinding algorithms, reinforcement learning, and other machine learning techniques to simulate more realistic behaviors. It can also enhance game balance by adjusting difficulty levels dynamically based on player skill and decisions to prevent boredom.

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