AI Model Mimics Human Goal-Setting Through Game Creation

AI Model Learns to Generate Human-Like Goals

Researchers at New York University have developed an innovative AI model capable of representing and generating human-like goals by learning from how people create games. This groundbreaking study, published in the journal Nature Machine Intelligence, could pave the way for AI systems that better understand human intentions and more accurately model and align with our goals 1

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The Challenge of Modeling Human Goal-Setting

Despite significant advancements in AI and computational work on goal-oriented behavior, current AI models fall short of capturing the richness and complexity of everyday human goals. To address this gap, the research team, led by Guy Davidson, an NYU doctoral student, focused on studying how humans create their own goals or tasks 1

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Davidson explains, "While goals are fundamental to human behavior, we know very little about how people represent and come up with them -- and lack models that capture the richness and creativity of human-generated goals" 2

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Experimental Approach

The researchers conducted a series of online experiments to capture how humans describe goal-setting actions:

1. Participants were placed in a virtual room containing various objects.
2. They were asked to propose a wide range of playful goals or games linked to the room's contents.
3. Nearly 100 games were recorded, forming a dataset from which the AI model learned 1

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Key Findings

The study revealed that human goal generation, while seemingly limitless, is guided by two primary principles:

1. Common sense: Goals must be physically plausible.
2. Recombination: New goals are created from shared gameplay elements 1

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For instance, participants created rules where a ball could realistically be thrown into a bin or bounced off a wall, and combined basic throwing elements to create various games 2

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AI Model Development and Evaluation

The researchers trained the AI model to create goal-oriented games using the rules and objectives developed by human participants. To assess the model's performance:

1. A new group of participants rated both human-generated and AI-produced games on attributes such as fun, creativity, and difficulty.
2. The results showed similar ratings for both human-created and AI-generated games, indicating that the model successfully captured human goal development processes 1

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Implications and Future Applications

This research provides a new framework for understanding how people create and represent goals, which could lead to:

1. More creative, original, and effective AI systems.
2. AI systems that can assist in designing human-like games and other playful activities.
3. Improved understanding of human goal formation and representation in computers 1

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The study, supported by grants from the National Science Foundation, involved contributions from Graham Todd, Julian Togelius, Todd M. Gureckis, and Brenden M. Lake, all affiliated with various departments at New York University 2

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