A Reinforcement Learning Approach to Predicting Human Design Actions Using a Data-Driven Reward Formulation

M. H. Rahman, A. E. Bayrak, Z. Sha

    Research output: Contribution to journalConference articlepeer-review

    3 Scopus citations

    Abstract

    In this paper, we develop a design agent based on reinforcement learning to mimic human design behaviours. A data-driven reward mechanism based on the Markov chain model is introduced so that it can reinforce prominent and beneficial design patterns. The method is implemented on a set of data collected from a solar system design problem. The result indicates that the agent provides higher prediction accuracy than the baseline Markov chain model. Several design strategies are also identified that differentiate high-performing designers from low-performing designers.

    Original languageEnglish
    Pages (from-to)1709-1718
    Number of pages10
    JournalProceedings of the Design Society
    Volume2
    DOIs
    StatePublished - May 2022
    Event17th International Design Conference, DESIGN 2022 - Virtual, Online, Croatia
    Duration: 23 May 202226 May 2022

    Keywords

    • artificial intelligence (AI)
    • design thinking
    • human behaviour

    Fingerprint

    Dive into the research topics of 'A Reinforcement Learning Approach to Predicting Human Design Actions Using a Data-Driven Reward Formulation'. Together they form a unique fingerprint.

    Cite this