Bayesian Artefact Design

  • Oulasvirta, Antti (Principal investigator)
  • Shin, Joongi (Project Member)
  • Hegemann, Lena (Project Member)
  • Todi, Kashyap (Project Member)
  • Putkonen, Aini-Maija (Project Member)
  • Halasinamara Chandramouli, Suyog (Project Member)
  • Hassinen, Heidi (Project Member)
  • Liao, Yi-Chi (Project Member)
  • Peng, Zhenhui (Project Member)
  • Dayama, Niraj (Project Member)
  • Leiva, Luis (Project Member)
  • Laine, Markku (Project Member)
  • Zhu, Yifan (Project Member)
  • Nioche, Aurélien (Project Member)

Project Details


The project advances computational design by establishing the methodological foundations for artificially intelligent design: human-level or supra-human generation of partial or full UI designs assuming access to behavioural data only (e.g., log data). The main objective is Bayesian Artefact Design, a reinforcement-learning-based formalism for AI agents that design artefacts for human use. AI agents learn to interpret people and predict the consequences of their interventions on them. Three challenging sub-objectives set this approach apart from existing approaches to computational design: (1) Agency: modeling the AI designer as an agent in the world that its designs are affecting; (2) Inference: the ability to observe and infer people's abilities, beliefs and intentions from behavioral data; (3) Speculativity: the ability to entertain possible designs by accurately predicting their consequences to people.
Effective start/end date01/09/201831/08/2023

Collaborative partners


Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.
  • Amortized Inference with User Simulations

    Moon, H. S., Oulasvirta, A. & Lee, B., 19 Apr 2023, CHI 2023 - Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. ACM, 20 p. 773

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    Open Access
    3 Citations (Scopus)
    6 Downloads (Pure)
  • Fragmented Visual Attention in Web Browsing: Weibull Analysis of Item Visit Times

    Putkonen, A., Nioche, A., Laine, M., Kuuramo, C. & Oulasvirta, A., 2023, Advances in Information Retrieval - 45th European Conference on Information Retrieval, ECIR 2023, Proceedings. Kamps, J., Goeuriot, L., Crestani, F., Maistro, M., Joho, H., Davis, B., Gurrin, C., Caputo, A. & Kruschwitz, U. (eds.). Springer, p. 62-78 17 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 13981 LNCS).

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    Open Access
    1 Citation (Scopus)
    43 Downloads (Pure)
  • Integrating AI in Human-Human Collaborative Ideation

    Shin, J., Koch, J., Lucero, A., Dalsgaard, P. & MacKay, W. E., 19 Apr 2023, p. 1-5. 5 p.

    Research output: Contribution to conferenceAbstractScientificpeer-review

    Open Access
    1 Citation (Scopus)