Skip to main content
SpringerLink
Log in

A Co-creation Interaction Framework and Its Application for Intelligent Design System

  • Conference paper
  • First Online:
Human-Computer Interaction. Theoretical Approaches and Design Methods (HCII 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13302))

Included in the following conference series:

  • 1995 Accesses

Abstract

In recent years, with the in-depth development of artificial intelligence technology in the field of creative design, intelligent systems have demonstrated certain creative capabilities. The computer is not just a tool for design expression, but transforms into a collaborator or creator of design. Co-creation between human and AI is the research focus in intelligent design system. At present, there is no established design framework that can describe the relevant components, features and their interrelationships in the intelligent design system, which can support system design and application in real application scenarios. Based on literature review and research practice, this paper discusses a co-creation interaction framework for intelligent design system. This framework links the source of human innovation with artificial intelligence technology through a collaborative mode, and describes an intelligent design system in which users and AI work together to complete innovation tasks. Then, taking the co-creative drawing system as an example, presented the implementation of the system, demonstrated the human-computer relationship in the framework, so as to provide a reference for related design intelligence research.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Martin, L., et al.: Event representations for automated story generation with deep neural nets. In: Proceedings of the AAAI Conference on Artificial Intelligence (2018)

    Google Scholar 

  2. Hadjeres, G., Pachet, F., Nielsen, F.: Deepbach: a steerable model for bach chorales generation. In: International Conference on Machine Learning, pp. 1362–1371. PMLR (2017)

    Google Scholar 

  3. Roberts, A., Engel, J., Eck, D.: Hierarchical variational autoencoders for music. In: NIPS Workshop on Machine Learning for Creativity and Design (2017)

    Google Scholar 

  4. Li, C., Wand, M.: Combining Markov random fields and convolutional neural networks for image synthesis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2479–2486 (2016)

    Google Scholar 

  5. Floridi, L.: Artificial intelligence, deepfakes and a future of ectypes. In: Ethics, Governance, and Policies in Artificial Intelligence. pp. 307–312. Springer, Cham (2021). https://doi.org/10.1007/s13347-018-0325-3

  6. Summerville, A., et al.: Procedural content generation via machine learning (PCGML). IEEE Trans. Games 10, 257–270 (2018)

    Article  Google Scholar 

  7. Yang, X., Mei, T., Xu, Y.-Q., Rui, Y., Li, S.: Automatic generation of visual-textual presentation layout. ACM Trans. Multimedia Comput. Commun. Appl. (TOMM) 12, 1–22 (2016)

    Google Scholar 

  8. Zhao, N., Cao, Y., Lau, R.W.: What characterizes personalities of graphic designs? ACM Trans. Graph. (TOG). 37, 1–15 (2018)

    Google Scholar 

  9. Pan, Y.: Heading toward artificial intelligence 2.0. Engineering 2, 409–413 (2016)

    Google Scholar 

  10. Zhu, J.-Y., Krähenbühl, P., Shechtman, E., Efros, A.A.: Generative visual manipulation on the natural image manifold. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) European Conference on Computer Vision, pp. 597–613. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46454-1_36

  11. Ha, D., Eck, D.: A neural representation of sketch drawings. arXiv preprint arXiv:1704.03477 (2017)

  12. Dou, Q., Zheng, X.S., Sun, T., Heng, P.-A.: Webthetics: quantifying webpage aesthetics with deep learning. Int. J. Hum Comput Stud. 124, 56–66 (2019)

    Article  Google Scholar 

  13. Shneiderman, B.: Creativity support tools: a grand challenge for HCI researchers. In: Engineering the User Interface, pp. 1–9. Springer, Cham (2009). https://doi.org/10.1007/978-1-84800-136-7_1

  14. Ren, X.: Rethinking the relationship between humans and computers. Computer 49, 104–108 (2016)

    Article  Google Scholar 

  15. Davis, N.M., Popova, Y., Sysoev, I., Hsiao, C.-P., Zhang, D., Magerko, B.: Building artistic computer colleagues with an enactive model of creativity. In: ICCC, pp. 38–45 (2014)

    Google Scholar 

  16. Lubart, T.: How can computers be partners in the creative process: classification and commentary on the special issue. Int. J. Hum Comput Stud. 63, 365–369 (2005)

    Article  Google Scholar 

  17. Allen, J.E., Guinn, C.I., Horvtz, E.: Mixed-initiative interaction. IEEE Intell. Syst. Applicat. 14, 14–23 (1999)

    Article  Google Scholar 

  18. Goldstein, I.M., Lawrence, J., Miner, A.S.: Human-machine collaboration in cancer and beyond: the centaur care model. JAMA Oncol. 3, 1303–1304 (2017)

    Article  Google Scholar 

  19. Feldman, S.: Co-creation: human and AI collaboration in creative expression. In: Electronic Visualisation and the Arts (EVA 2017), pp. 422–429 (2017)

    Google Scholar 

  20. Davis, N., Hsiao, C.-Pi., Singh, K.Y., Li, L., Moningi, S., Magerko, B.: Drawing apprentice: an enactive co-creative agent for artistic collaboration. In: Proceedings of the 2015 ACM SIGCHI Conference on Creativity and Cognition, pp. 185–186 (2015)

    Google Scholar 

  21. Oh, C., Song, J., Choi, J., Kim, S., Lee, S., Suh, B.: I lead, you help but only with enough details: Understanding user experience of co-creation with artificial intelligence. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pp. 1–13 (2018)

    Google Scholar 

  22. Kazi, R.H., Grossman, T., Cheong, H., Hashemi, A., Fitzmaurice, G.W.: DreamSketch: early stage 3D design explorations with sketching and generative design. In: UIST, pp. 401–414 (2017)

    Google Scholar 

  23. Guzdial, M., et al.: Friend, collaborator, student, manager: How design of an ai-driven game level editor affects creators. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, pp. 1–13 (2019)

    Google Scholar 

  24. Winograd, T.: Shifting viewpoints: Artificial intelligence and human–computer interaction. Artif. Intell. 170, 1256–1258 (2006)

    Article  Google Scholar 

  25. Liapis, A., Yannakakis, G.N., Alexopoulos, C., Lopes, P.: Can computers foster human users’ creativity? Theory and praxis of mixed-initiative co-creativity (2016)

    Google Scholar 

  26. Guzdial, M., Riedl, M.: An interaction framework for studying co-creative ai. arXiv preprint arXiv:1903.09709 (2019)

  27. Quanz, B., Sun, W., Deshpande, A., Shah, D., Park, J.: Machine learning based co-creative design framework. arXiv preprint arXiv:2001.08791 (2020)

  28. Buxton, W.: Human Skills in Interface Design. Wiley, New York (1994)

    Google Scholar 

  29. Markoff, J.: Machines of Loving Grace: The Quest for Common Ground between Humans and Robots. HarperCollins Publishers, New York (2016)

    Google Scholar 

  30. Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 679–698 (1986)

    Google Scholar 

  31. Belongie, S., Malik, J., Puzicha, J.: Shape context: a new descriptor for shape matching and object recognition. Adv. Neural Inf. Process. Syst. 13 (2000)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Alibaba Group, Hangzhou, 311121, China

    Zhiyuan Yang, Wenbo Yang, Guang Yang & Changyuan Yang

  2. Alibaba-Zhejiang University Joint Institute of Frontier Technologies, Hangzhou, 310027, China

    Zhiyuan Yang, Wenbo Yang, Guang Yang & Changyuan Yang

Authors
  1. Zhiyuan Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wenbo Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guang Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Changyuan Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Changyuan Yang .

Editor information

Editors and Affiliations

  1. The Open University of Japan, Chiba, Japan

    Masaaki Kurosu

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Yang, Z., Yang, W., Yang, G., Yang, C. (2022). A Co-creation Interaction Framework and Its Application for Intelligent Design System. In: Kurosu, M. (eds) Human-Computer Interaction. Theoretical Approaches and Design Methods. HCII 2022. Lecture Notes in Computer Science, vol 13302. Springer, Cham. https://doi.org/10.1007/978-3-031-05311-5_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-05311-5_24

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-05310-8

  • Online ISBN: 978-3-031-05311-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation