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Human computation games lack established ways of balancing the difficulty of tasks or levels served to players, potentially contributing to their low engagement rates. Traditional player rating systems have been suggested as a potential solution: using them to rate both players and tasks could estimate player skill and task difficulty and fuel player-task matchmaking. However, neither the effect of difficulty balancing on engagement in human computation games nor the use of player rating systems for this purpose has been empirically tested. We therefore examined the engagement effects of using the Glicko-2 player rating system to order tasks in the human computation game Paradox. An online experiment (n=294) found that both matchmaking-based and pure difficulty-based ordering of tasks led to significantly more attempted and completed levels than random ordering. Additionally, both matchmaking and random ordering led to significantly more difficult tasks being completed than pure difficulty-based ordering. We conclude that poor balancing contributes to poor engagement in human computation games, and that player rating system-based difficulty rating may be a viable and efficient way of improving both.

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In recent years, machine learning (ML) systems have been increasingly applied for performing creative tasks. Such creative ML approaches have seen wide use in the domains of visual art and music for applications such as image and music generation and style transfer. However, similar creative ML techniques have not been as widely adopted in the domain of game design despite the emergence of ML-based methods for generating game content. In this paper, we argue for leveraging and repurposing such creative techniques for designing content for games, referring to these as approaches for Game Design via Creative ML (GDCML). We highlight existing systems that enable GDCML and illustrate how creative ML can inform new systems via example applications and a proposed system.