Advertisement

On Interacting with Collective Knowledge of Group Facilitation

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8083)

Abstract

Group decision process design is a well-known class of ill-structured, dynamic, and going-concerns problem. The paper presents a human-computer interaction engineering approach to design a software prototype that provides personalized, contextual and actionable recommendations for this problem. The approach emphasizes the computational aspects of collective intelligence to structure these recommendations based on the collective knowledge that reflects not only the design space per se, but the collective experience in exploiting it as well. It is demonstrated by: 1) detailing the engineering issues of an implemented prototype for the group decision process design; and 2) explaining its functionalities through a representative set of interaction scenarios.

Keywords

design space exploration stigmergic systems human-computer interaction collective knowledge 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Vreede, G.J., Boonstra, J.A., Niederman, F.: What Is Effective GSS Facilitation? A Qualitative Inquiry Into Participants’ Perceptions. In: 35th Hawaiian Int. Conf. on System Sciences. IEEE Press, Los Alamitos (2002)Google Scholar
  2. 2.
    Lagroue, H.: The effectiveness of virtual facilitation in supporting GDSS appropriation and structured group decision making. Doctoral dissertation, Louisiana University (2006)Google Scholar
  3. 3.
    Sallam, R.L.: Who’s who in collaborative decision-making. Research Note No. G00214928. Gartner, Stamford (2011)Google Scholar
  4. 4.
    Heiko, T.: Cloud-Based Collaborative Decision Making. J. of Decision Support System Technology 4(4), 39–59 (2012)CrossRefGoogle Scholar
  5. 5.
    Turoff, M., Hiltz, S., Cho, H., Li, Z., Wang, Y.: Social decision support system. In: 35th Hawaiian Int. Conf. on System Sciences. IEEE Press, Los Alamitos (2002) Google Scholar
  6. 6.
    Rodriguez, M.A., Steinbock, D.J.: Group Holographic Modeling for Societal-Scale Decision-Making Systems. In: N. American Assoc. for Computational Social and Organizational Science Conf., Pittsburgh (2004)Google Scholar
  7. 7.
    Briggs, R., Kolfschoten, G., Vreede, G.J., Albrecht, C., Lukosch, S.: Facilitator in a Box. Information Systems, 1–10 (2010)Google Scholar
  8. 8.
    Zamfirescu, C.B., Duta, L., Candea, C.: Implementing the “Design for Emergence” Principle in GDSS. In: Frontiers in AI and Applications, vol. 212, pp. 61–72. IOS Press (2010)Google Scholar
  9. 9.
    Zamfirescu, C.B., Duta, L., Iantovics, B.: On Investigating the Cognitive Complexity of Designing the GDP. Studies in Informatics and Control 19, 263–270 (2010)Google Scholar
  10. 10.
    Zamfirescu, C.B., Filip, F.G.: Swarming models for facilitating collaborative decisions. International J. of Computers, Communications & Control 1, 1841–1844 (2010)Google Scholar
  11. 11.
    Parunak, V.D., Brueckner, S.A.: The Cognitive Aptitude of Swarming Agents (2009), https://activewiki.net/download/attachments/6258699/CASA.pdf
  12. 12.
    Rosen, D., Suthers, D.D.: Stigmergy and collaboration: Tracing the contingencies of mediated interaction. In: 44th Hawaiian Int. Conf. on System Sciences. IEEE Press (2011)Google Scholar
  13. 13.
    Heylighen, F.: Collective Intelligence and its Implementation on the Web: algorithms to develop a collective mental map. J. Comput. and Math. Org. Theory 5, 253–280 (1999)CrossRefzbMATHGoogle Scholar
  14. 14.
    Parunak, H.V.D., Brueckner, S.A., Matthews, R., Sauter, J.: Swarming methods for geospatial reasoning. Int. J. of Geographical Information Science 20(9), 945–964 (2006)CrossRefGoogle Scholar
  15. 15.
    Briggs, R.O., Vreede, G.J., Nunamaker Jr., J.F.: Collaboration Engineering with ThinkLets to Pursue Sustained Success with GSS. J. of Management Inf. Systems 19, 31–63 (2003)Google Scholar
  16. 16.
    Ender, G.: OpenSpace-Online: State-of-the-art Real-Time Conferencing (2009), http://www.openspace-online.com/OpenSpace-Online_eBook_en.pdf
  17. 17.
    Satnam, A.: Collective intelligence in action. Manning Publications (2008)Google Scholar
  18. 18.
    Zamfirescu, C.B., Candea, C.: A Stigmergic Guiding System to Facilitate the Group Decision Process. In: 28th IEEE Int. Conf. on Data Engineering, pp. 98–102. IEEE Press, Washington (2012)Google Scholar
  19. 19.
    Grosz, B., Kraus, S.: Collaborative plans for complex group action. Artificial Intelligence 86, 269–357 (1996)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Grosz, B., Kraus, S.: The Evolution of Shared Plans. In: Rao, A., Wooldridge, M. (eds.) Foundations of Rational Agency, pp. 227–262. Kluwer Academic Press (1999)Google Scholar
  21. 21.
    Ghallab, M., Nau, D., Traverso, P.: Hierarchical Task Network Planning. Automated Planning: Theory and Practice. Morgan Kaufmann (2004)Google Scholar
  22. 22.
    Object Management Group: Business Process Model and Notation (BPMN) Version 2.0. Document No. formal/2011-01-03 (2011)Google Scholar
  23. 23.
    Guerin, S., Kunkle, D.: Emergence of constraint in self-organizing systems. Nonlinear Dynamics, Psychology, and Life Sciences 8(2), 131–146 (2004)Google Scholar
  24. 24.
    Parunak, V.H.D., Brukner, S.: Entropy and Self-Organization in Multi-Agent Systems. In: Fifth Int. Conf. on Autonomous Agents, Montreal, pp. 124–130 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Faculty of Engineering, Department of Computer Science and Automatic Control“Lucian Blaga” University of SibiuSibiuRomania
  2. 2.Ropardo SRL, Research and Development DepartmentSibiuRomania

Personalised recommendations