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arXiv · 2101.06220

Player-AI Interaction: What Neural Network Games Reveal About AI as Play

Jichen Zhu·Jennifer Villareale·Nithesh Javvaji·Sebastian Risi·Mathias Löwe·Rush Weigelt·Casper Harteveld
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Abstract

The advent of artificial intelligence (AI) and machine learning (ML) bring human-AI interaction to the forefront of HCI research. This paper argues that games are an ideal domain for studying and experimenting with how humans interact with AI. Through a systematic survey of neural network games (n = 38), we identified the dominant interaction metaphors and AI interaction patterns in these games. In addition, we applied existing human-AI interaction guidelines to further shed light on player-AI interaction in the context of AI-infused systems. Our core finding is that AI as play can expand current notions of human-AI interaction, which are predominantly productivity-based. In particular, our work suggests that game and UX designers should consider flow to structure the learning curve of human-AI interaction, incorporate discovery-based learning to play around with the AI and observe the consequences, and offer users an invitation to play to explore new forms of human-AI interaction.

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Jichen Zhu, Jennifer Villareale, Nithesh Javvaji, Sebastian Risi, Mathias Löwe, Rush Weigelt, Casper Harteveld. 2021-01-15. Player-AI Interaction: What Neural Network Games Reveal About AI as Play. https://arxiv.org/abs/2101.06220

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