Boosting Mixed-Initiative Co-Creativity in Game Design: A Tutorial

2024-01-11

arXiv.org

AI Summary

This study addresses the low level of mixed-initiative co-creation (MI-CCy) in game design and the absence of systematic evaluation methods by proposing the first domain-specific MI-CCy guidance framework. Methodologically, we introduce the novel MI-CCy Quantifier—a visual assessment framework integrating human–computer collaboration theory, design pattern analysis, and multidimensional quantitative scaling—validated through representative case studies to identify common features of high-coordination tools and critical gaps in current practice. Key contributions include: (1) a reusable set of guidelines for MI-CCy development and evaluation; (2) a quantitative diagnostic tool enabling iterative optimization of co-creative systems; and (3) foundational theoretical insights and practical pathways for next-generation human–AI collaborative design systems.

Technology Category

• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Game Theory and Economic Paradigms: Cooperative Game Theory

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

In recent years, there has been a growing application of mixed-initiative co-creative approaches in the creation of video games. The rapid advances in the capabilities of artificial intelligence (AI) systems further propel creative collaboration between humans and computational agents. In this tutorial, we present guidelines for researchers and practitioners to develop game design tools with a high degree of mixed-initiative co-creativity (MI-CCy). We begin by reviewing a selection of current works that will serve as case studies and categorize them by the type of game content they address. We introduce the MI-CCy Quantifier, a framework that can be used by researchers and developers to assess co-creative tools on their level of MI-CCy through a visual scheme of quantifiable criteria scales. We demonstrate the usage of the MI-CCy Quantifier by applying it to the selected works. This analysis enabled us to discern prevalent patterns within these tools, as well as features that contribute to a higher level of MI-CCy. We highlight current gaps in MI-CCy approaches within game design, which we propose as pivotal aspects to tackle in the development of forthcoming approaches.

Problem

• Enhancing human-AI collaboration in game design
• Assessing co-creative tools with MI-CCy Quantifier
• Identifying gaps in mixed-initiative co-creativity approaches

Innovation

• Mixed-initiative co-creative game design tools
• MI-CCy Quantifier framework for assessment
• AI-enhanced human-computational agent collaboration