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SubmittedOpen Access (License Unspecified), Open
Conference proceeding
Player-AI Interaction: What Neural Network Games Reveal About AI as Play
CHI '21: PROCEEDINGS OF THE 2021 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS
01 Jan 2021
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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Details
- Title
- Player-AI Interaction: What Neural Network Games Reveal About AI as Play
- Creators
- Jichen Zhu - Drexel UniversityJennifer Villareale - Drexel UniversityNithesh Javvaji - Northeastern UniversitySebastian Risi - IT University of CopenhagenMathias Lowe - IT University of CopenhagenRush Weigelt - Drexel UniversityCasper Harteveld - Northeastern UniversityASSOC COMP MACHINERY
- Publication Details
- CHI '21: PROCEEDINGS OF THE 2021 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS
- Publisher
- Assoc Computing Machinery
- Number of pages
- 17
- Grant note
- IIS-1816470 / National Science Foundation (NSF); National Research Foundation of Korea 9145-00003B / DFF-Danish ERCprogramme
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Digital Media; Games, Artificial Intelligence, and Media Systems (GAIMS) Center
- Web of Science ID
- WOS:000758168003061
- Scopus ID
- 2-s2.0-105002581500
- Other Identifier
- 991019168201804721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Interdisciplinary Applications
- Computer Science, Software Engineering
- Computer Science, Theory & Methods