Facilitating Creativity in Collaborative Work with Computational Intelligence Software

Article English OPEN
Apostolou, D. ; Zachos, K. ; Maiden, N. ; Agell, N. ; Sanchez-Hernandez, G. ; Taramigkou, M. ; Star, K. ; Wippoo, M. (2016)

The use of computational intelligence for leveraging social creativity is a relatively new approach that allows organizations to find creative solutions to complex problems in which the interaction between stakeholders is crucial. The creative solutions that come from joint thinking-from the combined knowledge and abilities of people with diverse perspectives-contrast with traditional views of creativity that focus primarily on the individual as the main contributor of creativity. In an effort to support social creativity in organizations, in this paper we present computational intelligence software tools for that aim and an architecture for creating software mashups based on the concept of affinity space. The affinity space defines a digital setting to facilitate specific scenarios in collaborative business environments. The solution presented includes a set of free and open source software tools ranging from newly developed brainstorming applications to an expertise recommender for enhancing social creativity in the enterprise. The current paper addresses software design issues and presents reflections on the research work undertaken in the COLLAGE project between 2012 and 2015.
  • References (4)

    [8]  T.  Veale,  P.  Gervás,  and  A.  Pease,  “Understanding  creativity:  A  computational  perspective,” New Generation Computing, 24, pp. 203-207, 2006.  [10]  S.  Colton,  "The  painting  fool:  Stories  from  building  an automated  painter,"  Computers  and Creativity, Springer, pp. 3-38. 2012. 

    [11]  D.  Cope,  "Computational  Creativity  and  Music,"  Computational  Creativity  Research:  Towards Creative Machines, Atlantis Press, pp. 309-326. 2015.  [13]  F.  Pinel,  and  L.  R.  Varshney,  "Computational  creativity  for  culinary  recipes,"  CHI'14  Extended Abstracts on Human Factors in Computing Systems, pp. 439-442, ACM, 2014.  [14] J. E.  Perry‐Smith, and  C. E.  Shalley,  “The social side  of creativity:  A static and  dynamic  social network perspective,” The Academy of Management Review,  vol. 28, pp. 89-106, 2003.  [15]  A.  P.  Engelbrecht,  “Computational  Intelligence:  An  Introduction,”  John  Wiley  &  Sons,  2007. 

    [16]  M.  Ge,  C.  Delgado‐Battenfeld  and  D.  Jannach,  “Beyond  accuracy:  Evaluating  recommender systems by coverage and serendipity,” In Proc. of the fourth ACM conference  on Recommender systems, pp. 257-260, 2010.  [24]  Y.  Pan,  F.  Cong,  K.  Chen,  and  Y.  Yu,  “Diffusion‐aware  personalized  social  update  recommendation,” Proceedings of the 7th ACM conference on Recommender systems, pp. 69- 76, 2013. 

    [25] J. Chen, R. Nairn, L. Nelson, M. Bernstein, and E. Chi, “Short and tweet: experiments on  recommending  content  from  information  streams,”  In  Proc.  of  the  28th  international  conference on Human factors in computing systems, pp. 1185-1194, 2010.  [27]  K.  Ehrlich,  C.  Y.  Lin,  and  V.  Griffiths‐Fisher,  “Searching  for  experts  in  the  enterprise:   combining  text  and  social  network  analysis,”  In  Proc.  of  the  2007  international  ACM  conference on Supporting group work, pp. 117-126, 2007.  [29]  V.  Torra,  “The  weighted  OWA  operator,”  International  Journal  of  Intelligent  Systems,  12(2), pp. 153-166, 1997. 

  • Metrics
    0
    views in OpenAIRE
    0
    views in local repository
    80
    downloads in local repository

    The information is available from the following content providers:

    From Number Of Views Number Of Downloads
    City Research Online - IRUS-UK 0 80
Share - Bookmark