Fast Facts
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NYU scientists have developed a computer model that generates human-like goals by learning from how people create games, bridging a gap in understanding human goal creation and representation.
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Their research highlights that while human goal generation is creative, it is driven by a finite set of principles – common sense and recombination – allowing AI to emulate this process in goal-oriented tasks.
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The AI-generated games were evaluated by participants and received similar ratings to those created by humans in terms of fun, creativity, and difficulty, demonstrating the model’s effectiveness in capturing human-like goal setting.
- This innovative framework not only enhances our understanding of human goal formation but also holds potential for developing more effective AI systems and designing engaging games.
AI Generates Playful, Human-Like Games
New York University scientists have taken a significant step into the world of artificial intelligence. They developed a computer model that mimics how humans create playful games. This breakthrough, published in Nature Machine Intelligence, could reshape AI’s understanding of human intentions and goals.
While we are remarkably capable of generating our own goals, from childhood play to adult activities, current AI models struggle to grasp this complexity. Guy Davidson, the paper’s lead author and an NYU doctoral student, emphasizes that our understanding of goal representation is limited. "Our research provides a new framework for understanding how people create and represent goals," Davidson stated.
To explore human goal-setting, the researchers conducted online experiments. Participants were placed in a virtual room filled with various objects. They proposed nearly 100 games based on the items, such as bouncing a ball into a bin or building towers from blocks. This rich dataset allowed the AI model to learn the principles behind human goal creation.
Interestingly, the goals participants envisioned followed a few simple guides. They ensured goals were physically plausible and combined basic gameplay elements creatively. For example, a participant might propose a game in which a ball bounces off a wall before landing in a bin. Such guidelines helped the researchers understand the mechanics of goal generation.
After developing their model, the team needed to assess its effectiveness. They asked a new group of participants to rate games created by both humans and the AI. Ratings focused on attributes like fun, creativity, and difficulty. Surprisingly, participants rated AI-generated games similarly to human-made ones. This indicates that the AI successfully captured human creativity.
These findings reveal exciting possibilities for the future. As AI learns to design its own games, it can help create more engaging and interactive experiences. The research team included Graham Todd, Julian Togelius, Todd M. Gureckis, and Brenden M. Lake, all from various NYU departments. Their work received support from the National Science Foundation.
This innovative approach to understanding human-like goal generation not only enhances game design but also paves the way for more advanced AI systems in diverse fields. As we continue to unlock the potential of AI, the intersection of technology and creativity remains an inspiring frontier.
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