Predicting human design decisions with deep recurrent neural network combining static and dynamic data. (2020)

• Record Type: Journal Article
• Authors: Rahman, Molla Hafizur; Yuan, Shuhan; Xie, Charles; Sha, Zhenghui
• Is Part Of: Design science. Volume 6(2020)
• Publication Date: 2020
• Subjects: deep learning, -- sequential decision-making, -- recurrent neural network, -- design decision, -- design process; Design -- Research -- Periodicals; New products -- Management -- Periodicals; Design; Design -- Research; Electronic journals; Periodicals; 658.5752
• DOI: 10.1017/dsj.2020.12
• Languages: English
• ISSNs: 2053-4701

Abstract

Abstract : Computational modeling of the human sequential design process and successful prediction of future design decisions are fundamental to design knowledge extraction, transfer, and the development of artificial design agents. However, it is often difficult to obtain designer-related attributes (static data) in design practices, and the research based on combining static and dynamic data (design action sequences) in engineering design is still underexplored. This paper presents an approach that combines both static and dynamic data for human design decision prediction using two different methods. The first method directly combines the sequential design actions with static data in a recurrent neural network (RNN) model, while the second method integrates a feed-forward neural network that handles static data separately, yet in parallel with RNN. This study contributes to the field from three aspects: (a) we developed a method of utilizing designers' cluster information as a surrogate static feature to combine with a design action sequence in order to tackle the challenge of obtaining designer-related attributes; (b) we devised a method that integrates the function–behavior–structure design process model with the one-hot vectorization in RNN to transform design action data to design process stages where the insights into design thinking can be drawn; (c) to the best of our knowledge, it is the first time that two methods of combining static and dynamic data in RNN are compared, which provides new knowledge about the utility of different combination methods in studying sequential design decisions. The approach is demonstrated in two case studies on solar energy system design. The results indicate that with appropriate kernel models, the RNN with both static and dynamic data outperforms traditional models that only rely on design action sequences, thereby better supporting design research where static features, such as human characteristics, often play an important role.