Abstract
Generating novel and useful concepts is essential during the early design stage to explore a large variety of design opportunities, which usually requires advanced design thinking ability and a wide range of knowledge from designers. Growing works on computer-aided tools have explored the retrieval of knowledge and heuristics from design data. However, they only provide stimuli to inspire designers from limited aspects. This study explores the recent advance of the natural language generation (NLG) technique in the artificial intelligence (AI) field to automate the early stage design concept generation. Specifically, a novel approach utilizing the generative pretrained transformer (GPT) is proposed to leverage the knowledge and reasoning from textual data and transform them into new concepts in understandable language. Three concept generation tasks are defined to leverage different knowledge and reasoning: domain knowledge synthesis, problem-driven synthesis, and analogy-driven synthesis. The experiments with both human and data-driven evaluation show good performance in generating novel and useful concepts.
Issue Section:
Research Papers
Keywords:
artificial intelligence,
computational synthesis,
computer aided design,
human computer interfaces/interactions,
knowledge engineering
Topics:
Design
References
1.
Tschimmel
, K.
, 2012
, “Design Thinking As an Effective Toolkit for Innovation
,” ISPIM Conference Proceedings
, Barcelona, Spain
, June 17–20
, p. 1
.2.
Purcell
, A. T.
, and Gero
, J. S.
, 1996
, “Design and Other Types of Fixation
,” Des. Stud.
, 17
(4
), pp. 363
–383
. 3.
Linsey
, J. S.
, Tseng
, I.
, Fu
, K.
, Cagan
, J.
, Wood
, K. L.
, and Schunn
, C.
, 2010
, “A Study of Design Fixation, Its Mitigation and Perception in Engineering Design Faculty
,” ASME J. Mech. Des.
, 132
(4
), p. 041003
. 4.
Viswanathan
, V.
, Tomko
, M.
, and Linsey
, J.
, 2016
, “A Study on the Effects of Example Familiarity and Modality on Design Fixation
,” AI EDAM
, 30
(2
), pp. 171
–184
. 5.
Nishimoto
, K.
, Sumi
, Y.
, and Mase
, K.
, 1996
, “Toward an Outsider Agent for Supporting a Brainstorming Session—An Information Retrieval Method From a Different Viewpoint
,” Knowl.-Based Syst.
, 9
(6
), pp. 377
–384
. 6.
Buzan
, T.
, 1996
, The Mind Map Book: How to Use Radiant Thinking to Maximize Your Brain's Untapped Potential
, Penguin Books
, New York, NY
.7.
Altshuller
, G.
, 1999
, TRIZ, Systematic Innovation and Technical Creativity
, Technical Innovation Center
, Worcester, MA
.8.
Altshuller
, G.
, Shulyak
, L.
, Rodman
, S.
, and Fedoseev
, U.
, 1998
, 40 Principles: TRIZ Keys to Innovation
, Vol. 1
, Technical Innovation Center Inc.
, Worcester, MA
.9.
Luo
, J.
, Sarica
, S.
, and Wood
, K. L.
, 2019
, “Computer-Aided Design Ideation Using InnoGPS
,” International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
, ASME
, Paper No. V02AT03A011
.10.
Luo
, J.
, Sarica
, S.
, and Wood
, K. L.
, 2021
, “Guiding Data-Driven Design Ideation by Knowledge Distance
,” Knowl.-Based Syst.
, 218
, p. 106873
. 11.
Luo
, J.
, 2022
, “Data-Driven Innovation: What Is It
,” IEEE Trans. Eng. Manage.
, pp. 1
–7
. 12.
Ilevbare
, I. M.
, Probert
, D.
, and Phaal
, R.
, 2013
, “A Review of TRIZ, and Its Benefits and Challenges in Practice
,” Technovation
, 33
(2–3
), pp. 30
–37
. 13.
Yilmaz
, S.
, Daly
, S. R.
, Seifert
, C. M.
, and Gonzalez
, R.
, 2016
, “Evidence-Based Design Heuristics for Idea Generation
,” Des. Stud.
, 46
, pp. 95
–124
. 14.
Jin
, X.
, and Dong
, H.
, 2020
, “New Design Heuristics in the Digital Era
,” Proceedings of the Design Society: DESIGN Conference
, Virtual
, Oct. 26–29
, pp. 607
–616
.15.
Sarica
, S.
, Luo
, J.
, and Wood
, K. L.
, 2020
, “TechNet: Technology Semantic Network Based on Patent Data
,” Expert Syst. Appl.
, 142
, p. 112995
. 16.
Sarica
, S.
, Song
, B.
, Luo
, J.
, and Wood
, K. L.
, 2021
, “Idea Generation With Technology Semantic Network
,” AI EDAM
, 35
(3
), pp. 265
–283
. 17.
Chakrabarti
, A.
, Shea
, K.
, Stone
, R.
, Cagan
, J.
, Campbell
, M.
, Hernandez
, N. V.
, and Wood
, K. L.
, 2011
, “Computer-Based Design Synthesis Research: An Overview
,” ASME J. Comput. Inf. Sci. Eng.
, 11
(2
), p. 021003
. 18.
Wood
, K. L.
, and Greer
, J. L.
, 2001
, “Function-Based Synthesis Methods in Engineering Design,” Formal Engineering Design Synthesis
, E.
Antonsson
, and J.
Cagan
, eds., Cambridge University Press
, Cambridge, MA
, pp. 170
–227
.19.
Bohm
, M. R.
, Vucovich
, J. P.
, and Stone
, R. B.
, 2008
, “Using a Design Repository to Drive Concept Generation
,” ASME J. Comput. Inf. Sci. Eng.
, 8
(1
), p. 014502
. 20.
Sridharan
, P.
, and Campbell
, M. I.
, 2005
, “A Study on the Grammatical Construction of Function Structures
,” AI EDAM
, 19
(3
), pp. 139
–160
. 21.
Kurtoglu
, T.
, and Campbell
, M. I.
, 2009
, “Automated Synthesis of Electromechanical Design Configurations From Empirical Analysis of Function to Form Mapping
,” J. Eng. Des.
, 20
(1
), pp. 83
–104
. 22.
Sangelkar
, S.
, and McAdams
, D. A.
, 2017
, “Automated Graph Grammar Generation for Engineering Design With Frequent Pattern Mining
,” International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
, ASME
, Paper No. V02AT03A006
.23.
Liu
, Y. C.
, Chakrabarti
, A.
, and Bligh
, T.
, 2000
, “A Computational Framework for Concept Generation and Exploration in Mechanical Design,” Artificial Intelligence in Design’00
, J.S.
Gero
, ed., Springer Netherlands
, Dordrecht
, pp. 499
–519
. 24.
Chakrabarti
, A.
, and Bligh
, T. P.
, 2001
, “A Scheme for Functional Reasoning in Conceptual Design
,” Des. Stud.
, 22
(6
), pp. 493
–517
. 25.
Oh
, S.
, Jung
, Y.
, Kim
, S.
, Lee
, I.
, and Kang
, N.
, 2019
, “Deep Generative Design: Integration of Topology Optimization and Generative Models
,” ASME J. Mech. Des.
, 141
(11
), p. 111405
. 26.
Nie
, Z.
, Lin
, T.
, Jiang
, H.
, and Kara
, L. B.
, 2021
, “Topologygan: Topology Optimization Using Generative Adversarial Networks Based on Physical Fields Over the Initial Domain
,” ASME J. Mech. Des.
, 143
(3
), p. 031715
. 27.
Burnap
, A.
, Liu
, Y.
, Pan
, Y.
, Lee
, H.
, Gonzalez
, R.
, and Papalambros
, Y.
, 2016
, “Estimating and Exploring the Product Form Design Space Using Deep Generative Models
,” International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
, ASME
, Paper No. V02AT03A013
.28.
Yilmaz
, E.
, and German
, B.
, 2020
, “Conditional Generative Adversarial Network Framework for Airfoil Inverse Design
,” AIAA Aviation 2020 Forum
, Virtual
, June 15–19
, p. 3185
.29.
Li
, J.
, Xu
, K.
, Chaudhuri
, S.
, Yumer
, E.
, Zhang
, H.
, and Guibas
, L.
, 2017
, “Grass: Generative Recursive Autoencoders for Shape Structures
,” ACM Trans. Graph.
, 36
(4
), pp. 1
–14
. 30.
Shu
, D.
, Cunningham
, J.
, Stump
, G.
, Miller
, S. W.
, Yukish
, M. A.
, Simpson
, T. W.
, and Tucker
, C. S.
, 2020
, “3D Design Using Generative Adversarial Networks and Physics-Based Validation
,” ASME J. Mech. Des.
, 142
(7
), p. 071701
. 31.
Regenwetter
, L.
, Nobari
, A. H.
, and Ahmed
, F.
, 2022
, “Deep Generative Models in Engineering Design: A Review
,” ASME J. Mech. Des.
, 144
(7
), p. 071704
. 32.
Shah
, J. J.
, Vargas-Hernandez
, N. O. E.
, Summers
, J. D.
, and Kulkarni
, S.
, 2001
, “Collaborative Sketching (C-Sketch)—An Idea Generation Technique for Engineering Design
,” J. Creat. Behav.
, 35
(3
), pp. 168
–198
. 33.
Krasadakis
, G.
, 2020
, “Brainstorming,” The Innovation Mode
, Springer
, Cham
, pp. 141
–146
.34.
Bonnardel
, N.
, and Didier
, J.
, 2020
, “Brainstorming Variants to Favor Creative Design
,” Appl. Ergon.
, 83
, p. 102987
. 35.
Wang
, H. C.
, Li
, T. Y.
, Rosé
, C. P.
, Huang
, C. C.
, and Chang
, C. Y.
, 2006
, “VIBRANT: A Brainstorming Agent for Computer Supported Creative Problem Solving
,” International Conference on Intelligent Tutoring Systems
, Springer
, Berlin/Heidelberg
, pp. 787
–789
.36.
Lee
, B.
, Feldman
, B.
, and Fu
, K.
, 2022
, “Speech2Mindmap: Testing the Accuracy of Unsupervised Automatic Mindmapping Technology With Speech Recognition
,” ASME J. Mech. Des.
, 144
(2
), p. 021401
. 37.
Song
, B.
, Zurita
, N. F. S.
, Gyory
, J. T.
, Zhang
, G.
, McComb
, C.
, Cagan
, J.
, Stump
, G.
, et al, 2022
, “Decoding the Agility of Artificial Intelligence-Assisted Human Design Teams
,” Des. Stud.
, 79
, p. 101094
. 38.
Song
, B.
, Soria Zurita
, N. F.
, Nolte
, H.
, Singh
, H.
, Cagan
, J.
, and McComb
, C.
, 2022
, “When Faced With Increasing Complexity: The Effectiveness of Artificial Intelligence Assistance for Drone Design
,” ASME J. Mech. Des.
, 144
(2
), p. 021701
. 39.
Gyory
, J. T.
, Soria Zurita
, N. F.
, Martin
, J.
, Balon
, C.
, McComb
, C.
, Kotovsky
, K.
, and Cagan
, J.
, 2022
, “Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design
,” ASME J. Mech. Des.
, 144
(2
), p. 021405
. 40.
Fargnoli
, M.
, Rovida
, E.
, and Troisi
, R.
, 2006
, “The Morphological Matrix: Tool for the Development of Innovative Design Solutions
,” 4th International Conference on Axiomatic design, ICAD
, Florence, Italy
, June 13–16
, pp. 1
–7
.41.
Stone
, R. B.
, Wood
, K. L.
, and Crawford
, R. H.
, 2000
, “A Heuristic Method for Identifying Modules for Product Architectures
,” Des. Stud.
, 21
(1
), pp. 5
–31
. 42.
Jiang
, S.
, Hu
, J.
, Wood
, K. L.
, and Luo
, J.
, 2022
, “Data-Driven Design-By-Analogy: State-of-the-Art and Future Directions
,” ASME J. Mech. Des.
, 144
(2
), p. 020801
. 43.
He
, Y.
, Camburn
, B.
, Liu
, H.
, Luo
, J.
, Yang
, M.
, and Wood
, K.
, 2019
, “Mining and Representing the Concept Space of Existing Ideas for Directed Ideation
,” ASME J. Mech. Des.
, 141
(12
), p. 121101
. 44.
Siddharth
, L.
, Blessing
, L.
, Wood
, K. L.
, and Luo
, J.
, 2022
, “Engineering Knowledge Graph From Patent Database
,” ASME J. Comput. Inf. Sci. Eng.
, 22
(2
), p. 021008
. 45.
Han
, J.
, Sarica
, S.
, Shi
, F.
, and Luo
, J.
, 2022
, “Semantic Networks for Engineering Design: State of the Art and Future Directions
,” ASME J. Mech. Des.
, 144
(2
), p. 020802
. 46.
Sarica
, S.
, Han
, J.
, and Luo
, J.
, 2023
, “Design Representation as Semantic Networks
,” Comput. Ind.
, 144
, p. 103791
. 47.
Vattam
, S.
, and Goel
, A.
, 2013
, “An Information Foraging Model of Interactive Analogical Retrieval
,” Proceedings of the Annual Meeting of the Cognitive Science Society
, Berlin, Germany
, July 31–Aug. 3
.48.
Kruiper
, R.
, Vincent
, J. F.
, Abraham
, E.
, Soar
, R. C.
, Konstas
, I.
, Chen-Burger
, J.
, and Desmulliez
, M. P.
, 2018
, “Towards a Design Process for Computer-Aided Biomimetics
,” Biomimetics
, 3
(3
), p. 14
. 49.
Vattam
, S.
, Wiltgen
, B.
, Helms
, M.
, Goel
, A. K.
, and Yen
, J.
, 2011
, “DANE: Fostering Creativity in and through Biologically Inspired Design,” Design Creativity 2010
, T.
Taura
, and Y.
Nagai
, eds., Springer
, London
, pp. 115
–122
.50.
Deldin
, J. M.
, and Schuknecht
, M.
, 2014
, “The AskNature Database: Enabling Solutions in Biomimetic Design,” Biologically Inspired Design
, A.
Goel
, D.
McAdams
, and R.
Stone
, eds., Springer
, London
, pp. 17
–27
.51.
Goldschmidt
, G.
, and Smolkov
, M.
, 2006
, “Variances in the Impact of Visual Stimuli on Design Problem Solving Performance
,” Des. Stud.
, 27
(5
), pp. 549
–569
. 52.
Ahmed
, S.
, and Boelskifte
, P.
, 2006
, “Investigations of Product Design Engineering Students Intentions and a Users Perception of Product Character
,” Proceedings of the NordDesign 2006 Conference
, Reykjavik, Iceland
, Aug. 16–18
.53.
Han
, J.
, Forbes
, H.
, Shi
, F.
, Hao
, J.
, and Schaefer
, D.
, 2020
, “A Data-Driven Approach for Creative Concept Generation and Evaluation
,” Proceedings of the Design Society: DESIGN Conference
, Virtual
, Oct. 26–29
, Cambridge University Press, Vol. 1, pp. 167
–176
.54.
Jiang
, S.
, Luo
, J.
, Ruiz-Pava
, G.
, Hu
, J.
, and Magee
, C. L.
, 2021
, “Deriving Design Feature Vectors for Patent Images Using Convolutional Neural Networks
,” ASME J. Mech. Des.
, 143
(6
), p. 021008
. 55.
Wang
, L.
, Shen
, W.
, Xie
, H.
, Neelamkavil
, J.
, and Pardasani
, A.
, 2002
, “Collaborative Conceptual Design—State of the Art and Future Trends
,” Comput.-Aided Des.
, 34
(13
), pp. 981
–996
. 56.
Kvan
, T.
, 2000
, “Collaborative Design: What Is it?
,” Autom. Constr.
, 9
(4
), pp. 409
–415
. 57.
Vlah
, D.
, Žavbi
, R.
, and Vukašinović
, N.
, 2020
, “Evaluation of Topology Optimization and Generative Design Tools As Support for Conceptual Design
,” Proceedings of the Design Society: DESIGN Conference
, Virtual
, Oct. 26–29
, pp. 451
–460
.58.
Querin
, O. M.
, Victoria
, M.
, Alonso
, C.
, Loyola
, R. A.
, and Montrull
, P. M.
, 2017
, Topology Design Methods for Structural Optimization
, Elsevier
, London
.59.
Chau
, H. H.
, Chen
, X.
, McKay
, A.
, and Pennington
, A. D.
, 2004
, “Evaluation of a 3D Shape Grammar Implementation,” Design Computing and cognition’04
, J.S.
Gero
, ed., Springer Netherlands
, Dordrecht
, pp. 357
–376
. 60.
Hoisl
, F.
, and Shea
, K.
, 2011
, “An Interactive, Visual Approach to Developing and Applying Parametric Three-Dimensional Spatial Grammars
,” AI EDAM
, 25
(4
), pp. 333
–356
. 61.
Reid
, T. N.
, Gonzalez
, R. D.
, and Papalambros
, P. Y.
, 2010
, “Quantification of Perceived Environmental Friendliness for Vehicle Silhouette Design
,” ASME J. Mech. Des.
, 132
(10
), p. 101010
. 62.
Ren
, Y.
, Burnap
, A.
, and Papalambros
, P.
, 2013
, “Quantification of Perceptual Design Attributes Using a Crowd
,” DS 75-6: Proceedings of the 19th International Conference on Engineering Design (ICED13), Design for Harmonies, Vol. 6: Design Information and Knowledge
, Seoul, South Korea
, Aug. 19–22
, pp. 139
–148
.63.
Goodfellow
, I.
, Pouget-Abadie
, J.
, Mirza
, M.
, Xu
, B.
, Warde-Farley
, D.
, Ozair
, S.
, Courville
, A.
, and Bengio
, Y.
, 2014
, “Generative Adversarial Nets,” Advances in Neural Information Processing Systems
, Vol. 27
, Z.
Ghahramani
, M.
Welling
, C.
Cortes
, N.
Lawrence
, and K.Q.
Weinberger
, eds., Curran Associates, Inc.
, Red Hook, NY
, pp. 2672
–2680
.64.
Kingma
, D. P.
, & Welling
, M.
, 2013, “Auto-encoding Variational Bayes
,” arXiv preprint
. arxiv.org/abs/1312.611465.
Siddharth
, L.
, Blessing
, L.
, and Luo
, J.
, 2022
, “Natural Language Processing in-and-for Design Research
,” Des. Sci.
, 8
, p. E21
. 66.
Gatt
, A.
, and Krahmer
, E.
, 2018
, “Survey of the State of the Art in Natural Language Generation: Core Tasks, Applications and Evaluation
,” J. Artif. Intell. Res.
, 61
, pp. 65
–170
. 67.
Lopez
, A.
, 2008
, “Statistical Machine Translation
,” ACM Comput. Surv. (CSUR)
, 40
(3
), pp. 1
–49
. 68.
Ozsoy
, M. G.
, Alpaslan
, F. N.
, and Cicekli
, I.
, 2011
, “Text Summarization Using Latent Semantic Analysis
,” J. Inf. Sci.
, 37
(4
), pp. 405
–417
. 69.
Li
, Z.
, Jiang
, X.
, Shang
, L.
, and Li
, H.
, 2018
, “Paraphrase Generation With Deep Reinforcement Learning
,” Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
, Brussels, Belgium
, Oct. 31–Nov. 4
, pp. 3865
–3878
.70.
Leppänen
, L.
, Munezero
, M.
, Granroth-Wilding
, M.
, and Toivonen
, H.
, 2017
, “Data-Driven News Generation for Automated Journalism
,” Proceedings of the 10th International Conference on Natural Language Generation
, Santiago de Compostela, Spain
, Sept. 4–7
, pp. 188
–197
.71.
Wanner
, L.
, Bosch
, H.
, Bouayad-Agha
, N.
, Casamayor
, G.
, Ertl
, T.
, Hilbring
, D.
, Johansson
, L.
, et al, 2015
, “Getting the Environmental Information Across: From the Web to the User
,” Expert Syst.
, 32
(3
), pp. 405
–432
. 72.
Perera
, R.
, and Nand
, P.
, 2017
, “Recent Advances in Natural Language Generation: A Survey and Classification of the Empirical Literature
,” Comput. Inform.
, 36
(1
), pp. 1
–32
. 73.
Duan
, J.
, Zhao
, H.
, Zhou
, Q.
, Qiu
, M.
, and Liu
, M.
, 2020
, “A Study of Pre-trained Language Models in Natural Language Processing
,” 2020 IEEE International Conference on Smart Cloud (Smart-Cloud)
, Washington, DC
, Nov. 6–8
, pp. 116
–121
.74.
Vaswani
, A.
, Shazeer
, N.
, Parmar
, N.
, Uszkoreit
, J.
, Jones
, L.
, Gomez
, A. N.
, Kaiser
, Ł
, and Polosukhin
, I.
, 2017
, “Attention is All you Need,” Advances in Neural Information Processing Systems 30 (NIPS 2017)
, Vol. 30
, I.
Guyon
, U.
Von Luxburg
, S.
Bengio
, H.
Wallach
, R.
Fergus
, S.
Vishwanathan
, and R.
Garnett
, eds., Curran Associates, Inc.
, Red Hook, NY
.75.
Topal
, M. O.
, Bas
, A.
, and van Heerden
, I.
, 2021
, “Exploring Transformers in Natural Language Generation: GPT, BERT, and XLNET
,” International Conference on Interdisciplinary Applications of AI (ICIDAAI)
, Virtual
, May 21–23
.76.
Pascanu
, R.
, Mikolov
, T.
, and Bengio
, Y.
, 2013
, “On the Difficulty of Training Recurrent Neural Networks
,” International Conference on Machine Learning
, Atlanta, GA
, June 17–19
, PMLR, pp. 1310
–1318
.77.
Brown
, T. B.
, Mann
, B.
, Ryder
, N.
, Subbiah
, M.
, Kaplan
, J. D.
, Dhariwal
, P.
, Neelakantan
, A.
, et al, 2020
, “Language Models are Few-Shot Learners,” Advances in Neural Information Processing Systems 33 (NeurIPS 2020)
, Vol. 33
, H.
Larochelle
, M.
Ranzato
, R.
Hadsell
, M.F.
Balcan
, and H.
Lin
, eds., Curran Associates, Inc.
, Red Hook, NY
, pp. 1877
–1901
.78.
Radford
, A.
, Wu
, J.
, Child
, R.
, Luan
, D.
, Amodei
, D.
, and Sutskever
, I.
, 2019
, Language Models are Unsupervised Multitask Learners, Technical Report, OpenAI
.79.
Peng
, B.
, Zhu
, C.
, Li
, C.
, Li
, X.
, Li
, J.
, Zeng
, M.
, and Gao
, J.
, 2020
, “Few-shot Natural Language Generation for Task-Oriented Dialog,” Findings of the Association for Computational Linguistics: EMNLP 2020
, T.
Cohn
, Y.
He
, and Y.
Liu
, eds., Association for Computational Linguistics
, Online
, pp. 172
–182
. 80.
Amin-Nejad
, A.
, Ive
, J.
, and Velupillai
, S.
, 2020
, “Exploring Transformer Text Generation for Medical Dataset Augmentation
,” Proceedings of the 12th Language Resources and Evaluation Conference
, Marseille, France
, May 13–15
, pp. 4699
–4708
.81.
Lee
, J. S.
, and Hsiang
, J.
, 2020
, “Patent Claim Generation by Fine-Tuning OpenAI GPT-2
,” World Pat. Inf.
, 62
, p. 101983
. 82.
Fang
, J.
, 2021
, “An Application of Customized GPT-2 Text Generator for Modern Content Creators
,” Master thesis
, https://escholarship.org/uc/item/3rd9v7xm83.
Radford
, A.
, Narasimhan
, K.
, Salimans
, T.
, and Sutskever
, I.
, 2018
, Improving Language Understanding by Generative Pre-training, Technical Report, OpenAI
.84.
Hinton
, G. E.
, and Salakhutdinov
, R. R.
, 2006
, “Reducing the Dimensionality of Data With Neural Networks
,” Science
, 313
(5786
), pp. 504
–507
. 85.
Huang
, Q.
, Gan
, Z.
, Celikyilmaz
, A.
, Wu
, D.
, Wang
, J.
, and He
, X.
, 2019
, “Hierarchically Structured Reinforcement Learning for Topically Coherent Visual Story Generation
,” Proceedings of the AAAI Conference on Artificial Intelligence
, Honolulu, HI
, Jan. 27–Feb. 1
, Vol. 33, No. 1, pp. 8465
–8472
.86.
Devlin
, J.
, Chang
, M. W.
, Lee
, K.
, and Toutanova
, K.
, 2019
, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
,” Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
, Minneapolis, MN
, June 2–7
, pp. 4171
–4186
.87.
Papineni
, K.
, Roukos
, S.
, Ward
, T.
, and Zhu
, W. J.
, 2002
, “Bleu: A Method for Automatic Evaluation of Machine Translation
,” Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics
, Philadelphia, PA
, July 7–12
, pp. 311
–318
.88.
Lin
, C. Y.
, 2004
, “Rouge: A Package for Automatic Evaluation of Summaries,” Text Summarization Branches Out
, Association for Computational Linguistics
, Barcelona, Spain
, pp. 74
–81
.89.
Banerjee
, S.
, and Lavie
, A.
, 2005
, “METEOR: An Automatic Metric for MT Evaluation With Improved Correlation With Human Judgments
,” Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization
, Ann Arbor, MI
, June 29
, pp. 65
–72
.90.
Kusner
, M.
, Sun
, Y.
, Kolkin
, N.
, and Weinberger
, K.
, 2015
, “From Word Embeddings to Document Distances
,” International Conference on Machine Learning
, Lille, France
, July 7–9
, PMLR, pp. 957
–966
.91.
Zhao
, W.
, Peyrard
, M.
, Liu
, F.
, Gao
, Y.
, Meyer
, C. M.
, and Eger
, S.
, 2019
, “MoverScore: Text Generation Evaluating With Contextualized Embeddings and Earth Mover Distance
,” Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the Ninth International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
, Hong Kong, China
, Nov. 3–7
, pp. 563
–578
.92.
Lowe
, R.
, Noseworthy
, M.
, Serban
, I. V.
, Angelard-Gontier
, N.
, Bengio
, Y.
, and Pineau
, J.
, 2017
, “Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses
,” Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Volume 1: Long Papers
, Vancouver, Canada
, July 30–Aug. 4
, pp. 1116
–1126
.93.
Zhou
, W.
, and Xu
, K.
, 2020
, “Learning to Compare for Better Training and Evaluation of Open Domain Natural Language Generation Models
,” Proceedings of the AAAI Conference on Artificial Intelligence
, New York
, Feb. 7–12
, Vol. 34, No. 5, pp. 9717
–9724
.94.
Bender
, E. M.
, Gebru
, T.
, McMillan-Major
, A.
, and Shmitchell
, S.
, 2021
, “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big
,” Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency
, Virtual
, Mar. 3–10
, pp. 610
–623
.95.
Mednick
, S.
, 1962
, “The Associative Basis of the Creative Process
,” Psychol. Rev.
, 69
(3
), pp. 220
–232
. 96.
Beaty
, R. E.
, Kenett
, Y. N.
, Hass
, R. W.
, and Schacter
, D. L.
, 2022
, “Semantic Memory and Creativity: The Costs and Benefits of Semantic Memory Structure in Generating Original Ideas
,” Think. Reason.
, pp. 1
–35
. 97.
Simonton
, D. K.
, 2000
, “Creativity: Cognitive, Personal, Developmental, and Social Aspects
,” Am. Psychol.
, 55
(1
), pp. 151
–158
. 98.
He
, Y.
, and Luo
, J.
, 2017
, “The Novelty ‘Sweet Spot’ of Invention
,” Des. Sci.
, 3
, p. e21
. 99.
Luo
, J.
, and Wood
, K. L.
, 2017
, “The Growing Complexity in Invention Process
,” Res. Eng. Des.
, 28
(4
), pp. 421
–435
. 100.
Stevenson
, C.
, Smal
, I.
, Baas
, M.
, Grasman
, R.
, and van der Maas
, H.
, 2022
, “Putting GPT-3′s Creativity to the (Alternative Uses) Test
,” arXiv preprint arXiv:2206.08932.101.
Amabile
, T. M.
, 1982
, “Social Psychology of Creativity: A Consensual Assessment Technique
,” J. Pers. Soc. Psychol.
, 43
(5
), pp. 997
–1013
. 102.
Weisberg
, R. W.
, 2006
, Creativity: Understanding Innovation in Problem Solving, Science, Invention, and the Arts
, John Wiley & Sons, Inc.
, Hoboken, NJ
.103.
Amabile
, T. M.
, 1996
, Creativity in Context: Update to the Social Psychology of Creativity
, Westview Press
, Boulder, CO
.104.
Shah
, J. J.
, Smith
, S. M.
, and Vargas-Hernandez
, N.
, 2003
, “Metrics for Measuring Ideation Effectiveness
,” Des. Stud.
, 24
(2
), pp. 111
–134
. 105.
Brown
, D. C.
, 2015
, “Computational Design Creativity Evaluation,” Design Computing and Cognition'14
, J.S.
Gero
, and S.
Hanna
, eds., Springer
, Cham
, pp. 207
–224
. 106.
He
, Y.
, and Luo
, J.
, 2017
, “Novelty, Conventionality, and Value of Invention,” Design Computing and Cognition'16
, J.
Gero
, ed., Springer
, Cham
, pp. 23
–38
. 107.
Camburn
, B.
, He
, Y.
, Raviselvam
, S.
, Luo
, J.
, and Wood
, K.
, 2020
, “Machine Learning-Based Design Concept Evaluation
,” ASME J. Mech. Des.
, 142
(3
), p. 031113
. 108.
Shibayama
, S.
, Yin
, D.
, and Matsumoto
, K.
, 2021
, “Measuring Novelty in Science With Word Embedding
,” PLoS One
, 16
(7
), p. e0254034
. 109.
Teller
, V.
, 2000
, Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
, Prentice Hall
, Upper Saddle River, NJ
.110.
Beaty
, R. E.
, and Johnson
, D. R.
, 2021
, “Automating Creativity Assessment With SemDis: An Open Platform for Computing Semantic Distance
,” Behav. Res. Methods
, 53
(2
), pp. 757
–780
. 111.
Luo
, J.
, Song
, B.
, Blessing
, L.
, and Wood
, K.
, 2018
, “Design Opportunity Conception Using the Total Technology Space Map
,” AI EDAM
, 32
(4
), pp. 449
–461
. 112.
Srinivasan
, V.
, Song
, B.
, Luo
, J.
, Subburaj
, K.
, Elara
, M. R.
, Blessing
, L.
, and Wood
, K.
, 2018
, “Does Analogical Distance Affect Performance of Ideation?
,” ASME J. Mech. Des.
, 140
(7
), p. 071101
. 113.
Gentner
, D.
, 1983
, “Structure-Mapping: A Theoretical Framework for Analogy
,” Cogn. Sci.
, 7
(2
), pp. 155
–170
. 114.
Radford
, A.
, Kim
, J. W.
, Hallacy
, C.
, Ramesh
, A.
, Goh
, G.
, Agarwal
, S.
, Sastry
, G.
, et al, 2021
, “Learning Transferable Visual Models From Natural Language Supervision
,” International Conference on Machine Learning
, Virtual
, July 18–24
, PMLR, pp. 8748
–8763
.115.
Ramesh
, A.
, Pavlov
, M.
, Goh
, G.
, Gray
, S.
, Voss
, C.
, Radford
, A.
, Chen
, M.
, and Sutskever
, I.
, 2021
, “Zero-Shot Text-to-Image Generation
,” International Conference on Machine Learning
, Virtual
, July 18–24
, PMLR, pp. 8821
–8831
.116.
Dosovitskiy
, A.
, Beyer
, L.
, Kolesnikov
, A.
, Weissenborn
, D.
, Zhai
, X.
, Unterthiner
, T.
, Dehghani
, M.
, et al, 2021
, “An Image Is Worth 16×16 Words: Transformers for Image Recognition at Scale
,” International Conference on Learning Representations
, Virtual
, May 3–7
.117.
He
, K.
, Chen
, X.
, Xie
, S.
, Li
, Y.
, Dollár
, P.
, and Girshick
, R.
, 2021
, “Masked Autoencoders Are Scalable Vision Learners
,” arXiv preprint arXiv:2111.06377
.118.
Bommasani
, R.
, Hudson
, D. A.
, Adeli
, E.
, Altman
, R.
, Arora
, S.
, von Arx
, S.
, Bernstein
, M. S.
, et al, 2021
, “On the Opportunities and Risks of Foundation Models
,” arXiv preprint
. https://arxiv.org/abs/2108.07258Copyright © 2023 by ASME
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