An artificial intelligence based data-driven approach for design ideation

Chen, Liuqing; Wang, Pan; Dong, Hao; Shi, Feng; Han, Jiarui; Guo, Yike; Childs, Peter; Xiao, Jun; Wu, Chao

doi:10.1016/j.jvcir.2019.02.009

Cited by 100 publications

(60 citation statements)

References 53 publications

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“… Chen et al (2019) presented a system to aid design ideation. The framework consists of two separate networks: a semantic ideation network and a visual concepts synthesis network.…”

Section: Discussionmentioning

confidence: 99%

Generative Adversarial Networks–Enabled Human–Artificial Intelligence Collaborative Applications for Creative and Design Industries: A Systematic Review of Current Approaches and Trends

Hughes

Zhu

Bednarz

2021

Front. Artif. Intell.

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The future of work and workplace is very much in flux. A vast amount has been written about artificial intelligence (AI) and its impact on work, with much of it focused on automation and its impact in terms of potential job losses. This review will address one area where AI is being added to creative and design practitioners’ toolbox to enhance their creativity, productivity, and design horizons. A designer’s primary purpose is to create, or generate, the most optimal artifact or prototype, given a set of constraints. We have seen AI encroaching into this space with the advent of generative networks and generative adversarial networks (GANs) in particular. This area has become one of the most active research fields in machine learning over the past number of years, and a number of these techniques, particularly those around plausible image generation, have garnered considerable media attention. We will look beyond automatic techniques and solutions and see how GANs are being incorporated into user pipelines for design practitioners. A systematic review of publications indexed on ScienceDirect, SpringerLink, Web of Science, Scopus, IEEExplore, and ACM DigitalLibrary was conducted from 2015 to 2020. Results are reported according to PRISMA statement. From 317 search results, 34 studies (including two snowball sampled) are reviewed, highlighting key trends in this area. The studies’ limitations are presented, particularly a lack of user studies and the prevalence of toy-examples or implementations that are unlikely to scale. Areas for future study are also identified.

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“… Chen et al (2019) presented a system to aid design ideation. The framework consists of two separate networks: a semantic ideation network and a visual concepts synthesis network.…”

Section: Discussionmentioning

confidence: 99%

Generative Adversarial Networks–Enabled Human–Artificial Intelligence Collaborative Applications for Creative and Design Industries: A Systematic Review of Current Approaches and Trends

Hughes

Zhu

Bednarz

2021

Front. Artif. Intell.

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“…Network metrics have provided a medium to derive useful design-related insights from the structure of the graphs, and various layout methods have provided ways of representing the design-related data in an easily comprehensible way (Lim et al, 2016;. For example, network visualizations have been utilized to represent the whole technology space to support innovation and competitive intelligence (Luo et al, 2017(Luo et al, , 2018Sarica, Yan, et al, 2020), show the relations between components and subsystems to evalute designs (He and Luo, 2017;Pasqual and De Weck, 2012;Sosa et al, 2007) and inform design decisions (Kim and Kim, 2012;Sosa et al, 2007), discover the patterns of design activities (Alstott et al, 2017;Cash et al, 2014;Cash and Štorga, 2015), reveal the structure of design document repositories to guide retrievals (Fu et al, 2013;Luo et al, 2021), and represent mind maps (Camburn, Arlitt, et al, 2020;Camburn, He, et al, 2020) and concept networks (Chen et al, 2019;Chen and Krishnamurthy, 2020;Liu et al, 2020;Sarica et al, 2019Sarica et al, , 2021Shi et al, 2017;Song, Evans, et al, 2020;Souili et al, 2015) for design ideation uses. On the other hand, a few studies explored other visualization methods such as word-clouds (He, Camburn, Liu, et al, 2019; based on design description texts.…”

Section: Related Workmentioning

confidence: 99%

Design Knowledge Representation With Technology Semantic Network

Sarica

Luo

2021

Proc. Des. Soc.

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Engineers often need to discover and learn designs from unfamiliar domains for inspiration or other particular uses. However, the complexity of the technical design descriptions and the unfamiliarity to the domain make it hard for engineers to comprehend the function, behavior, and structure of a design. To help engineers quickly understand a complex technical design description new to them, one approach is to represent it as a network graph of the design-related entities and their relations as an abstract summary of the design. While graph or network visualizations are widely adopted in the engineering design literature, the challenge remains in retrieving the design entities and deriving their relations. In this paper, we propose a network mapping method that is powered by Technology Semantic Network (TechNet). Through a case study, we showcase how TechNet’s unique characteristic of being trained on a large technology-related data source advantages itself over common-sense knowledge bases, such as WordNet and ConceptNet, for design knowledge representation.

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“…Computational creativity refers to a system that exhibits behaviours that unbiased observers would deem to be creative (Colton & Wiggins 2012). Since deep learning has become more prevalent and powerful in the computer science field, systems have become more intelligent and able to complete creative tasks, such as visual art, poetry, music and design (Loughran & O'Neill 2016;Chen et al 2019). By summarizing perspectives from psychology, philosophy, cognitive science and computer science as to how creativity can be measured both in humans and in computers, Lamb et al (2018) make recommendations for how to evaluate computational creativity from perspectives including person, process, product and press.…”

Section: Deep Learning For Designmentioning

confidence: 99%

Neurocognition-inspired design with machine learning

Wang

Peng

et al. 2020

Des. Sci.

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Generating designs via machine learning has been an on-going challenge in computer-aided design. Recently, deep learning methods have been applied to randomly generate images in fashion, furniture and product design. However, such deep generative methods usually require a large number of training images and human aspects are not taken into account in the design process. In this work, we seek a way to involve human cognitive factors through brain activity indicated by electroencephalographic measurements (EEG) in the generative process. We propose a neuroscience-inspired design with a machine learning method where EEG is used to capture preferred design features. Such signals are used as a condition in generative adversarial networks (GAN). First, we employ a recurrent neural network Long Short-Term Memory as an encoder to extract EEG features from raw EEG signals; this data are recorded from subjects viewing several categories of images from ImageNet. Second, we train a GAN model conditioned on the encoded EEG features to generate design images. Third, we use the model to generate design images from a subject’s EEG measured brain activity. To verify our proposed generative design method, we present a case study, in which the subjects imagine the products they prefer, and the corresponding EEG signals are recorded and reconstructed by our model for evaluation. The results indicate that a generated product image with preference EEG signals gains more preference than those generated without EEG signals. Overall, we propose a neuroscience-inspired artificial intelligence design method for generating a design taking into account human preference. The method could help improve communication between designers and clients where clients might not be able to express design requests clearly.

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An artificial intelligence based data-driven approach for design ideation

Cited by 100 publications

References 53 publications

Generative Adversarial Networks–Enabled Human–Artificial Intelligence Collaborative Applications for Creative and Design Industries: A Systematic Review of Current Approaches and Trends

Generative Adversarial Networks–Enabled Human–Artificial Intelligence Collaborative Applications for Creative and Design Industries: A Systematic Review of Current Approaches and Trends

Design Knowledge Representation With Technology Semantic Network

Neurocognition-inspired design with machine learning

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