""AI and games have been a couple for the last seventy years,""
""But," he said, "there's so much more we can do with AI than simply play better than humans.""
""Imagine a racing game where your opponent makes an intentional mistake because you're about to give up," he explained. "That's the kind of AI I want to build - AI that understands you.""
Early AI milestones in games prioritized superhuman performance, with systems trained to beat humans at chess, Go, or competitive video games. The focus has shifted toward collaboration and building agents that model player emotion, intention, and experience. Player experience modeling captures behavior and affect to enable experience-driven generative AI that dynamically adapts game worlds to a player's emotional state or skill level. Empathetic agents can modulate difficulty, slow down when frustration appears, or increase challenge when boredom sets in. Human feedback and player data are essential to train models that understand players and personalize gameplay in real time.
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