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

Published online by Cambridge University Press:  26 May 2022

M. H. Rahman ,
A. E. Bayrak  and
Z. Sha
M. H. Rahman
Affiliation:
University of Arkansas, United States of America
A. E. Bayrak
Affiliation:
Stevens Institute of Technology, United States of America
Z. Sha*
Affiliation:
The University of Texas at Austin, United States of America
*
[email protected]

Abstract

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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.

Keywords

artificial intelligence (AI)human behaviourdesign thinking
Type
Article
Information
Proceedings of the Design Society , Volume 2: DESIGN2022 , May 2022 , pp. 1709 - 1718
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2022.

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