Research Area: Computational Models of Creative Designing


The apogee of designing is to be creative. How can computation support creative designing? This research focuses on computation models of creative designing. It takes two viewpoints: that creativity is in the designing process and that creativity requires social interactions. It develops a model of creativity based on the notion of expanding the state space of possible designs.

Novel models of processes to expand the space of possible designs have been developed. These include analogy from distant domains, behavioral analogy, reverse engineering of emergent structures and expanding genetic operators. Creative results have been produced.

Funding comes from DARPA and NSF.

Projects include:
  • creativity through re-interpretation
  • situated analogy
  • genetic engineering processes for creativity
  • modelling societal factors in creativity
  • ontology of creativity
  • integrating design science, computer science, cognitive science and neuroscience to study creativity




Papers that describe some of the research methods and some of the main fundings

  • Gero, JS (1990) Design prototypes: a knowledge representation schema for design, AI Magazine 11(4): 26-36. (pdf)
  • Gero, JS (2011) A situated cognition view of innovation with implications for innovation policy, in K Husbands-Fealing, J Lane, J Marburger, S Shipp and B Valdez (eds), The Science of Science Policy: A Handbook, Stanford University Press, pp. 104-119. (pdf)
  • Gero, JS (2017) Generalizing ekphrastic expression: A foundation for a computational method to aid creative design, in P. Janssen, P. Loh, A. Raonic, M. A. Schnabel (eds.), Protocols, Flows and Glitches, Proceedings of the 22nd International Conference of the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA) 2017, pp. 345-354. (pdf)
  • Kannengiesser, U and Gero, JS (2015) Is designing independent of domain? Comparing models of engineering, software and service design, Research in Engineering Design 26(3): 253-275. DOI: 10.1007/s00163-015-0195-y (pdf)
  • Kelly, N and Gero, JS (2017) Generate and situated transformation as a paradigm for models of computational creativity, International Journal of Design Creativity and Innovation 5(3-4): 149-167. DOI:10.1080/21650349.2016.1203821 (pdf)
  • Saunders, R and Gero, JS (2002) How to study artificial creativity, in T Hewett and T Kavanagh (eds), Creativity and Cognition 2002, ACM Press, New York, NY, pp. 80-87. (pdf)
  • Saunders, R and Gero, JS (2004) Situated design simulations using curious agents, AIEDAM 18 (2): 153-161. (pdf)
  • Singh, V, Dong, A and Gero, JS (2012) Computational studies to understand the role of social learning in team familiarity and their effects on team performance, CoDesign 8(1) DOI:10.1080/15710882.2011.63308 (link)
  • Sosa, R and Gero, JS (2005) Social models of creativity: Integrating the DIFI and FBS frameworks to study creative design, in Gero, JS and Maher, ML (eds), Computational and Cognitive Models of Creative Design VI, Key Centre of Design Computing and Cognition, University of Sydney, pp. 19-44. (pdf)
  • Sosa, R and Gero, JS (2005) A computational study of creativity in design, AIEDAM 19(4): 229-244. (pdf).
  • Sosa, R and Gero, JS (2012) Brainstorming in solitude and teams: A computational study of the role of group influence, in ML Maher, K Hammond, A Pease, R Pérez y Pérez, D Ventura and G Wiggins (eds), Proceedings of the Third International Conference on Computational Creativity, pp.188-194. (pdf)
  • Thomas, RC and Gero, JS (2012) Patterns of social influence in networks of social cognitive agents, Collective Intelligence 2012 (to appear) (pdf)

Papers that describe this research in more detail

  • Gero, J. S. (2000) Computational models of innovative and creative design processes, Technological Forecasting and Social Change 64:183-196.
  • Gero, JS (1996) Creativity, emergence and evolution in design: concepts and framework, Knowledge-Based Systems 9(7): 435-448. (pdf)
  • Gero, JS and Kazakov, V (1999) Using analogy to extend the behaviour state space in creative design, in JS Gero and ML Maher (eds), Computational Models of Creative Design IV, Key Centre of Design Computing and Cognition, University of Sydney, Sydney, Australia, pp. 113-143. (pdf)
  • Gero, JS and Kazakov, V (1999) An extrapolation process for creative designing, in G Augenbroe and CM Eastman (eds), Computers in Building, Kluwer, Boston, pp. 263-274. (pdf)
  • Gero, JS and Kumar, B (1993) Expanding design spaces through new design variables, Design Studies 14(2): 210-221. (pdf)
  • Gero, JS and Maher, ML (1991) Mutation and analogy to support creativity in computer-aided design, in GN Schmitt (ed), CAAD Futures '91, ETH, Zurich, pp. 241-249. (pdf)
  • Gero, JS and Sosa, R (2007) Complexity measures as a basis for mass customisation of novel designs, Environment and Planning B (to appear) (pdf)
  • McLaughlin, S and Gero, JS (1989) Requirements of a reasoning system to support creative design, Knowledge-Based Systems 2(1): 62-71.
  • Kelly, N and Gero, JS (2012) Computational modelling of the design conversation as a sequence of situated acts, in T Fischer, K De Biswas, J Ham, R Naka and W Huang (eds) Beyond Codes and Pixels CAADRIA2012, CAADRIA, Hong Kong, pp.121-130 . (pdf)
  • Qian, L and Gero, JS (1996) Function-behaviour-structure paths and their role in analogy-based design, AIEDAM 10:289-312.

For the rest you can scour my publications starting with those under In Progress.


The people who have or are working on this include:

  • Andres Gomez
  • Kaz Grace
  • Sushil Louis
  • Udo Kannengiesser
  • Vladimir Kazakov
  • Nick Kelly
  • Jarek Kulinski
  • Bimal Kumar
  • Mary Lou Maher
  • Sally McLaughlin
  • Lena Qian
  • Mike Rosenman
  • Rob Saunders
  • Thorsten Schnier
  • Vishal Singh
  • Ricardo Sosa