May Ai? Design ideation with cooperative contextual bandits

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorKoch, Janinen_US
dc.contributor.authorLucero, Andrésen_US
dc.contributor.authorHegemann, Lenaen_US
dc.contributor.authorOulasvirta, Anttien_US
dc.contributor.departmentDepartment of Communications and Networkingen
dc.contributor.departmentDepartment of Designen
dc.contributor.departmentDepartment of Mediaen
dc.contributor.groupauthorUser Interfacesen
dc.contributor.groupauthorHelsinki Institute for Information Technology (HIIT)en
dc.contributor.organizationDepartment of Communications and Networkingen_US
dc.date.accessioned2019-08-15T08:19:54Z
dc.date.available2019-08-15T08:19:54Z
dc.date.issued2019-05-02en_US
dc.description| openaire: EC/H2020/637991/EU//COMPUTED
dc.description.abstractDesign 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.en
dc.description.versionPeer revieweden
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationKoch, 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/10.1145/3290605.3300863en
dc.identifier.doi10.1145/3290605.3300863en_US
dc.identifier.isbn9781450359702
dc.identifier.otherPURE UUID: 061d00eb-e219-44c2-b76d-97ef55d9b42aen_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/061d00eb-e219-44c2-b76d-97ef55d9b42aen_US
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85067625961&partnerID=8YFLogxKen_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/35738592/ELEC_Koch_May_AI_ACM_SIGHI.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/39609
dc.identifier.urnURN:NBN:fi:aalto-201908154654
dc.language.isoenen
dc.relationinfo:eu-repo/grantAgreement/EC/H2020/637991/EU//COMPUTEDen_US
dc.relation.ispartofACM SIGCHI Annual Conference on Human Factors in Computing Systemsen
dc.relation.ispartofseriesCHI 2019 - Proceedings of the 2019 CHI Conference on Human Factors in Computing Systemsen
dc.relation.ispartofseriesConference on Human Factors in Computing Systems - Proceedingsen
dc.rightsopenAccessen
dc.subject.keywordCreativity support toolsen_US
dc.subject.keywordIdeation supporten_US
dc.subject.keywordInteractive machine-learningen_US
dc.subject.keywordMood board designen_US
dc.titleMay Ai? Design ideation with cooperative contextual banditsen
dc.typeConference article in proceedingsfi
dc.type.versionpublishedVersion
Files