Adapting User Interfaces with Model-based Reinforcement Learning
| dc.contributor | Aalto-yliopisto | fi |
| dc.contributor | Aalto University | en |
| dc.contributor.author | Todi, Kashyap | en_US |
| dc.contributor.author | Leiva, Luis | en_US |
| dc.contributor.author | Bailly, Gilles | en_US |
| dc.contributor.author | Oulasvirta, Antti | en_US |
| dc.contributor.department | Department of Communications and Networking | en |
| dc.contributor.groupauthor | User Interfaces | en |
| dc.contributor.groupauthor | Helsinki Institute for Information Technology (HIIT) | en |
| dc.contributor.organization | Sorbonne Université | en_US |
| dc.contributor.organization | University of Luxembourg | en_US |
| dc.date.accessioned | 2021-09-22T06:31:17Z | |
| dc.date.available | 2021-09-22T06:31:17Z | |
| dc.date.issued | 2021-05-06 | en_US |
| dc.description.abstract | Adapting an interface requires taking into account both the positive and negative effects that changes may have on the user. A carelessly picked adaptation may impose high costs to the user – for example, due to surprise or relearning effort – or "trap" the process to a suboptimal design immaturely. However, effects on users are hard to predict as they depend on factors that are latent and evolve over the course of interaction. We propose a novel approach for adaptive user interfaces that yields a conservative adaptation policy: It finds beneficial changes when there are such and avoids changes when there are none. Our model-based reinforcement learning method plans sequences of adaptations and consults predictive HCI models to estimate their effects. We present empirical and simulation results from the case of adaptive menus, showing that the method outperforms both a non-adaptive and a frequency-based policy. | en |
| dc.description.version | Peer reviewed | en |
| dc.format.extent | 13 | |
| dc.format.mimetype | application/pdf | en_US |
| dc.identifier.citation | Todi, K, Leiva, L, Bailly, G & Oulasvirta, A 2021, Adapting User Interfaces with Model-based Reinforcement Learning . in CHI '21: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems : Making Waves, Combining Strengths ., 573, ACM, ACM SIGCHI Annual Conference on Human Factors in Computing Systems, Yokohama, Japan, 08/05/2021 . https://doi.org/10.1145/3411764.3445497 | en |
| dc.identifier.doi | 10.1145/3411764.3445497 | en_US |
| dc.identifier.isbn | 978-1-4503-8096-6 | |
| dc.identifier.other | PURE UUID: 5c9cc5ef-e241-4960-a7ae-b3b308aa879c | en_US |
| dc.identifier.other | PURE ITEMURL: https://research.aalto.fi/en/publications/5c9cc5ef-e241-4960-a7ae-b3b308aa879c | en_US |
| dc.identifier.other | PURE LINK: http://www.scopus.com/inward/record.url?scp=85104074543&partnerID=8YFLogxK | en_US |
| dc.identifier.other | PURE FILEURL: https://research.aalto.fi/files/55480130/Todi_Adapting_user_interfaces_with_model_basedCHI2021Adaptive.pdf | en_US |
| dc.identifier.uri | https://aaltodoc.aalto.fi/handle/123456789/110093 | |
| dc.identifier.urn | URN:NBN:fi:aalto-202109229316 | |
| dc.language.iso | en | en |
| dc.relation.ispartofseries | CHI '21: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems | en |
| dc.rights | openAccess | en |
| dc.subject.keyword | Adaptive User Interfaces | en_US |
| dc.subject.keyword | Reinforcement Learning | en_US |
| dc.subject.keyword | Predictive Models | en_US |
| dc.subject.keyword | Monte Carlo Tree Search | en_US |
| dc.title | Adapting User Interfaces with Model-based Reinforcement Learning | en |
| dc.type | A4 Artikkeli konferenssijulkaisussa | fi |
| dc.type.version | publishedVersion |
