Advertisement

Introduction and Challenges of Environment Architectures for Collective Intelligence Systems

  • Juergen MusilEmail author
  • Angelika Musil
  • Stefan Biffl
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9068)

Abstract

Collective Intelligence Systems (CIS), such as wikis, social networks, and content-sharing platforms, are an integral part of today’s collective knowledge creation and sharing processes. CIS are complex adaptive systems, which realize environment-mediated coordination, in particular with stigmergic mechanisms. The behavior of CIS is emergent, as high-level, system-wide behavior is influenced by low-level rules. These rules are encapsulated by the CIS infrastructure that comprises in its center an actor-created artifact network that stores the shared content. In this chapter, we provide an introduction to the CIS domain, CIS architectural principles and processes. Further, we reflect on the role of CIS as multi-agent system (MAS) environments and conclude with an outlook on research challenges for CIS architectures.

Keywords

Collective intelligence Coordination Self-organization Software architecture Stigmergic information system Stigmergy 

Notes

Acknowledgments

This work was supported by the Christian Doppler Forschungsgesellschaft, the Federal Ministry of Economy and Science, and the National Foundation for Research, Technology and Development, Austria.

References

  1. 1.
    Bedau, M.A., Humphreys, P.: Emergence: Contemporary Readings in Philosophy and Science. MIT Press, London (2008)CrossRefGoogle Scholar
  2. 2.
    Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, Oxford (1999)zbMATHGoogle Scholar
  3. 3.
    Brun, Y., Di Marzo Serugendo, G., Gacek, C., Giese, H., Kienle, H., Litoiu, M., Müller, H., Pezzè, M., Shaw, M.: Engineering self-adaptive systems through feedback loops. In: Cheng, B.H.C., de Lemos, R., Giese, H., Inverardi, P., Magee, J. (eds.) Software Engineering for Self-Adaptive Systems. LNCS, vol. 5525, pp. 48–70. Springer, Heidelberg (2009) CrossRefGoogle Scholar
  4. 4.
    Bush, V.: As we may think. The Atlantic 176(1), 101–108 (1945)Google Scholar
  5. 5.
    Ciancarini, P.: Coordination models and languages as software integrators. ACM Comput. Surv. 28(2), 300–302 (1996)CrossRefGoogle Scholar
  6. 6.
    Engelbart, D.C.: Augmenting human intellect: a conceptual framework. Technical report, Stanford Research Institute, Menlo Park, CA (1962). http://www.dougengelbart.org/pubs/papers/scanned/Doug_Engelbart-AugmentingHumanIntellect.pdf
  7. 7.
    Erman, L.D., Hayes-Roth, F., Lesser, V.R., Reddy, D.R.: The Hearsay-II speech-understanding system: integrating knowledge to resolve uncertainty. ACM Comput. Surv. 12(2), 213–253 (1980)CrossRefGoogle Scholar
  8. 8.
    Esparcia, S., Argente, E.: A functional taxonomy for artifacts. In: Corchado, E., Graña Romay, M., Manhaes Savio, A. (eds.) HAIS 2010, Part II. LNCS, vol. 6077, pp. 159–167. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  9. 9.
    Franklin, S.: Coordination without communication. Technical report, Institute for Intelligent Systems, University of Memphis (1996). http://ccrg.cs.memphis.edu/~franklin/coord.html
  10. 10.
    Gelernter, D.: Generative communication in Linda. ACM Trans. Program. Lang. Syst. 7(1), 80–112 (1985)CrossRefzbMATHGoogle Scholar
  11. 11.
    Gelernter, D., Carriero, N.: Coordination languages and their significance. Commun. ACM 35(2), 96–107 (1992)CrossRefGoogle Scholar
  12. 12.
    Grassé, P.P.: La reconstruction du nid et les coordinations inter-individuelles chez Bellicositermes natalensis et Cubitermes sp. La théorie de la stigmergie: Essai d’interprétation du comportement des Termites constructeurs. Insectes Soc. 6(1), 41–80 (1959)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Grasso, A., Convertino, G.: Collective intelligence in organizations: tools and studies. Comput. Support. Coop. Work (CSCW) 21(4–5), 357–369 (2012)CrossRefGoogle Scholar
  14. 14.
    Gruber, T.: Collective knowledge systems: where the social web meets the semantic web. Semant. Web Web 2.0 6(1), 4–13 (2008)CrossRefGoogle Scholar
  15. 15.
    Heylighen, F.: Stigmergy as a Universal Coordination Mechanism: components, varieties and application. In: Lewis, T., Marsh, L. (eds.) Human Stigmergy: Theoretical Developments and New Applications. Springer, Berlin (2015). http://pespmc1.vub.ac.be/papers/stigmergy-varieties.pdf Google Scholar
  16. 16.
    ISO/IEC/IEEE 42010: Systems and Software Engineering - Architecture Description (2011). http://www.iso-architecture.org/ieee-1471/index.html
  17. 17.
    Lévy, P.: Collective Intelligence: Mankind’s Emerging World in Cyberspace. Perseus Books, Cambridge (1997)Google Scholar
  18. 18.
    Licklider, J.C.R.: Man-computer symbiosis. IRE Trans. Hum. Factors Electron. HFE 1(1), 4–11 (1960)CrossRefGoogle Scholar
  19. 19.
    Lykourentzou, I., Vergados, D.J., Kapetanios, E., Loumos, V.: Collective intelligence systems: classification and modeling. J. Emerg. Technol. Web Intell. 3(3), 217–226 (2011)Google Scholar
  20. 20.
    Malone, T.W., Bernstein, M.S. (eds.): Handbook of Collective Intelligence. MIT Press (2015) (in press)Google Scholar
  21. 21.
    Malone, T.W., Laubacher, R., Dellarocas, C.: Harnessing Crowds : Mapping the Genome of Collective Intelligence (2009) (Working Paper No. 2009–001). http://cci.mit.edu/publications/CCIwp2009-01.pdf
  22. 22.
    Malone, T.W., Laubacher, R., Dellarocas, C.: The collective intelligence genome. MIT Sloan Manag. Rev. 51(3), 21–31 (2010)Google Scholar
  23. 23.
    Miorandi, D., Maltese, V., Rovatsos, M., Nijholt, A., Stewart, J. (eds.): Social Collective Intelligence: Combining the Powers of Humans and Machines to Build a Smarter Society. Springer International Publishing, Switzerland (2014)Google Scholar
  24. 24.
    Musil, J., Musil, A., Biffl, S.: Towards a coordination-centric architecture metamodel for social web applications. In: Avgeriou, P., Zdun, U. (eds.) ECSA 2014. LNCS, vol. 8627, pp. 106–113. Springer, Heidelberg (2014) Google Scholar
  25. 25.
    Musil, J., Musil, A., Weyns, D., Biffl, S.: An Architecture framework for collective intelligence systems. In: Proceedings of the 12th Working IEEE/IFIP Conference on Software Architecture (WICSA 2015), pp. 21–30. IEEE Computer Society (2015)Google Scholar
  26. 26.
    Musil, J., Musil, A., Winkler, D., Biffl, S.: A first account on stigmergic information systems and their impact on platform development. In: Proceedings of the WICSA/ECSA 2012 Companion Volume (WICSA/ECSA 2012), pp. 69–73. ACM (2012)Google Scholar
  27. 27.
    Omicini, A., Contucci, P.: Complexity and interaction: blurring borders between physical, computational, and social systems. In: Bǎdicǎ, C., Nguyen, N.T., Brezovan, M. (eds.) ICCCI 2013. LNCS, vol. 8083, pp. 1–10. Springer, Heidelberg (2013) Google Scholar
  28. 28.
    Omicini, A., Ricci, A., Viroli, M.: Artifacts in the A&A meta-model for multi-agent systems. Auton. Agents Multi-Agent Syst. 17(3), 432–456 (2008)CrossRefGoogle Scholar
  29. 29.
    Papadopoulos, G.A., Arbab, F.: Coordination models and languages. Adv. Comput. 46, 329–400 (1998)CrossRefGoogle Scholar
  30. 30.
    Ricci, A., Omicini, A., Viroli, M., Gardelli, L., Oliva, E.: Cognitive stigmergy: towards a framework based on agents and artifacts. In: Weyns, D., Van Dyke Parunak, H., Michel, F. (eds.) E4MAS 2006. LNCS (LNAI), vol. 4389, pp. 124–140. Springer, Heidelberg (2007) CrossRefGoogle Scholar
  31. 31.
    Salminen, J.: Collective intelligence in humans: a literature review. In: Proceedings of Collective Intelligence Conference 2012 (2012). eprint arXiv:1204.3401, http://arxiv.org/abs/1204.3401
  32. 32.
    Schmidt, K., Simone, C.: Coordination mechanisms: towards a conceptual foundation of CSCW systems design. Comput. Support. Coop. Work (CSCW) 5(2–3), 155–200 (1996)CrossRefGoogle Scholar
  33. 33.
    Smart, P., Simperl, E., Shadbolt, N.: A taxonomic framework for social machines. In: Miorandi, D., Maltese, V., Rovatsos, M., Nijholt, A., Stewart, J. (eds.) Social Collective Intelligece, pp. 51–85. Springer International Publishing, Switzerland (2014)Google Scholar
  34. 34.
    Surowiecki, J.: The Wisdom of Crowds. Abacus , London (2005)Google Scholar
  35. 35.
    Susi, T., Ziemke, T.: Social cognition, artefacts, and stigmergy: a comparative analysis of theoretical frameworks for the understanding of artefact-mediated collaborative activity. Cogn. Syst. Res. 2(4), 273–290 (2001)CrossRefGoogle Scholar
  36. 36.
    Tapscott, D., Williams, A.D.: Wikinomics: How Mass Collaboration Changes Everything. Portfolio, New York (2006)Google Scholar
  37. 37.
    Van Dyke Parunak, H.: A survey of environments and mechanisms for human-human stigmergy. In: Weyns, D., Van Dyke Parunak, H., Michel, F. (eds.) E4MAS 2005. LNCS (LNAI), vol. 3830, pp. 163–186. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  38. 38.
    Weyns, D., Omicini, A., Odell, J.: Environment as a first class abstraction in multiagent systems. Auton. Agents Multi-Agent Syst. 14(1), 5–30 (2007)CrossRefGoogle Scholar
  39. 39.
    Zambonelli, F.: Engineering self-organizing urban superorganisms. Eng. Appl. Artif. Intell. 41, 325–332 (2015)CrossRefGoogle Scholar
  40. 40.
    Zambonelli, F., Omicini, A., Anzengruber, B., Castelli, G., De Angelis, F.L., Serugendo, G.D.M., Dobson, S., Fernandez-Marquez, J.L., Ferscha, A., Mamei, M., Mariani, S., Molesini, A., Montagna, S., Nieminen, J., Pianini, D., Risoldi, M., Rosi, A., Stevenson, G., Viroli, M., Ye, J.: Developing pervasive multi-agent systems with nature-inspired coordination. Pervasive Mobile Comput. 17, 236–252 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Institute of Software Technology and Interactive Systems, CDL-FlexVienna University of TechnologyViennaAustria

Personalised recommendations