May Ai? Design ideation with cooperative contextual bandits
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Date
2019-05-02
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Language
en
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CHI 2019 - Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, Conference on Human Factors in Computing Systems - Proceedings
Abstract
Design ideation is a prime creative activity in design. However, it is challenging to support computationally due to its quickly evolving and exploratory nature. The paper presents cooperative contextual bandits (CCB) as a machine-learning method for interactive ideation support. A CCB can learn to propose domain-relevant contributions and adapt their exploration/exploitation strategy. We developed a CCB for an interactive design ideation tool that 1) suggests inspirational and situationally relevant materials (“may AI?”); 2) explores and exploits inspirational materials with the designer; and 3) explains its suggestions to aid reflection. The application case of digital mood board design is presented, wherein visual inspirational materials are collected and curated in collages. In a controlled study, 14 of 16 professional designers preferred the CCB-augmented tool. The CCB approach holds promise for ideation activities wherein adaptive and steerable support is welcome but designers must retain full outcome control.Description
| openaire: EC/H2020/637991/EU//COMPUTED
Keywords
Creativity support tools, Ideation support, Interactive machine-learning, Mood board design
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Citation
Koch, J, Lucero, A, Hegemann, L & Oulasvirta, A 2019, May Ai? Design ideation with cooperative contextual bandits . in CHI 2019 - Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems . ACM, ACM SIGCHI Annual Conference on Human Factors in Computing Systems, Glasgow, United Kingdom, 04/05/2019 . https://doi.org/10.1145/3290605.3300863
