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- W3124385306 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." @default.
- W3124385306 created "2021-02-01" @default.
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- W3124385306 date "2021-01-15" @default.
- W3124385306 modified "2023-09-25" @default.
- W3124385306 title "Player-AI Interaction: What Neural Network Games Reveal About AI as Play" @default.
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- W3124385306 doi "https://doi.org/10.48550/arxiv.2101.06220" @default.
- W3124385306 hasPublicationYear "2021" @default.
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