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

• URL: http://arxiv.org/abs/2101.06220v2
• Date: Mon, 18 Jan 2021 10:25:19 GMT
• Title: Player-AI Interaction: What Neural Network Games Reveal About AI as Play
• Authors: Jichen Zhu, Jennifer Villareale, Nithesh Javvaji, Sebastian Risi, Mathias L\"owe, Rush Weigelt, Casper Harteveld
• Abstract summary: 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, we identified the dominant interaction metaphors and AI interaction patterns. Our work suggests that game and UX designers should consider flow to structure the learning curve of human-AI interaction.
• Score: 14.63311356668699
• License: http://creativecommons.org/licenses/by/4.0/
• 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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