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World and Human Action Models towards gameplay ideation.

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Generative artificial intelligence (AI) can enhance creative ideation by supporting iterative design. A new model, WHAM, generates consistent gameplay and user modifications, aligning AI with creative practices.

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Area of Science:

  • Computer Science
  • Human-Computer Interaction
  • Artificial Intelligence

Background:

  • Generative AI offers potential for creative industries but faces challenges in supporting core creative practices like iterative tweaking and divergent thinking.
  • Current generative AI models inadequately support essential human creative processes, limiting their integration into creative workflows.

Purpose of the Study:

  • To align generative AI model development with user needs in creative practices, specifically within game development.
  • To introduce and evaluate a novel generative AI model that addresses limitations in supporting iterative and divergent creative processes.

Main Methods:

  • Developed and evaluated the World and Human Action Model (WHAM), a generative AI model tailored for creative support.
  • Focused on user needs within game development to guide the AI's capabilities, emphasizing consistency, diversity, and persistence of user modifications.

Main Results:

  • WHAM demonstrates the capability to generate consistent and diverse gameplay sequences.
  • The model successfully persists user modifications, a critical feature for aligning AI with iterative creative workflows.
  • WHAM learns relevant structure from data, enabling broader applications compared to previous domain-specific tools.

Conclusions:

  • Generative AI, exemplified by WHAM, can be effectively developed and evaluated based on user needs to support creative ideation and practices.
  • The demonstrated capabilities of WHAM represent a significant advancement in AI-powered creativity support tools, particularly in dynamic domains like game development.