A Reinforcement Learning Approach to Predicting Human Design Actions Using a Data-Driven Reward Formulation. (May 2022)

• Record Type: Journal Article
• Authors: Rahman, M. H.; Bayrak, A. E.; Sha, Z.
• Is Part Of: Proceedings of the Design Society. Volume 2(2022)
• Page Start: 1709
• Page End: 1718
• Publication Date: 2022-05
• Subjects: artificial intelligence (AI) -- human behaviour -- design thinking; Industrial design -- Congresses; Engineering design -- Congresses; 620.0042
• DOI: 10.1017/pds.2022.173
• Languages: English
• ISSNs: 2633-7762

Abstract

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.