Visual modeling means to model a system and its components, relationships and interactions, structures and patterns, etc. by setting up visual building blocks. Visual modeling adopts reductionism methodology and focuses on modeling each part, and the interaction between parts, to achieve an overall picture of a complex system.


Multiagent System Agent Environment Agent System Markov Decision Process Complex Adaptive System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Erl, T.: Service-Oriented Architecture: A Field Guide to Integrating XML and Web Services. Pearson Education, Upper Saddle River (2004)Google Scholar
  2. 2.
    Zambonelli, F., Jennings, N.R., Wooldridge, M.: Developing multiagent systems: the GAIA methodology. ACM Trans. Softw. Eng. Methodol. 12(3), 317–370 (2003)Google Scholar
  3. 3.
    Weiss, G. (ed.): Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence. The MIT Press, Cambridge, MA (2013)Google Scholar
  4. 4.
    Odell, J.J., Parunak, H.V.D., Fleischer, M., Brueckner, S.: Modeling agents and their environment. In: Proceedings of the 3rd International Conference on Agent-Oriented Software Engineering III, AOSE2002, pp. 16–31, Springer, Berlin/Heidelberg (2002)Google Scholar
  5. 5.
    Vasilyev, A.: Synergetic approach in adaptive systems. Master thesis, Transport and Telecommunication Institute, Riga, Latvia (2002)Google Scholar
  6. 6.
    Castro, J., Kolp, M., Mylopoulos, J.: Towards requirements-driven information systems engineering: the TROPOS project. Inf. Syst. 27(6), 365–389 (2002)MATHGoogle Scholar
  7. 7.
    Dai, R.W., Cao, L.B.: Internet—an open complex giant system, Science in China (Series E). Sci. China Ser. E 33(4), 289–296 (2003) (in Chinese)Google Scholar
  8. 8.
    Albert, R., Barabási, A.L.: Topology of evolving networks: local events and universality. Phys. Rev. Lett. 85, 5234 (2000)Google Scholar
  9. 9.
    Albert, R., Barabási, A.L., Jeong, H., Bianconi, G.: Power-law distribution of the World Wide Web. Science 287, 2115 (2000)Google Scholar
  10. 10.
    Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286, 509–512 (1999)MathSciNetGoogle Scholar
  11. 11.
    Waldrop, M.M.: Complexity: The Emerging Science at the Edge of Order and Chaos. Simon & Schuster, New York (1992)Google Scholar
  12. 12.
    Beer, R.D.: A dynamical systems perspective on agent-environment interaction. Artif. Intell. 72, 173–215 (1995)Google Scholar
  13. 13.
    FIPA Interaction Protocol Library Specification, Foundation for Intelligent Physical Agents, Scholar
  14. 14.
    Jennings, N.R.: On agent-based software engineering. Artif. Intell. 117(2), 277–296 (2000)MATHGoogle Scholar
  15. 15.
    Alexander, C.: The Timeless Way of Building. Oxford University Press, New York (1979)Google Scholar
  16. 16.
  17. 17.
    Gamma, E., Helm, R., Johnson, R., Vlissides, J.: Design Patterns. Addison-Wesley, Reading, MA (1995)Google Scholar
  18. 18.
  19. 19.
  20. 20.
    Hannemann, J., Kiczales, G.: Design pattern implementation in Java and AspectJ. In: Proceedings of the 17th AM SIGPLAN Conference on Object-oriented Programming, Systems, Languages and Applications, OOPSLA’02, pp. 161–173, ACM, New York (2002)Google Scholar
  21. 21.
  22. 22.
    Grand, M. (ed.): Java Enterprise Design Patterns: Patterns in Java, vol. 3. Wiley India, Mumbai (2002)Google Scholar
  23. 23.
    Huget, M.P., Koning, J.L.: Interaction Protocol Engineering in Multiagent Systems. LNCS/LNAI State-of-the-Art Survey-2650. Springer, Berlin/Heidelberg (2003)Google Scholar
  24. 24.
    Odell, J.J., Parunak, H.V.D., Bauer, B.: Representing agent interaction protocols in UML. In: Proceedings of the 1st International Workshop on Agent-Oriented Software Engineering, AOSE2000, pp. 121–140, Springer, Berlin/Heidelberg (2001)Google Scholar
  25. 25.
    Paurobally, S., Cunningham, J., Jennings, N.R.: Developing agent interaction protocols using graphical and logical methodologies. In: Workshop on Programming MAS, Second International Conference on Autonomous Agents and Multiagent Systems, AAMAS’03 Melbourne, Australia, 14–18 July 2003Google Scholar
  26. 26.
    Bauer, B., Müller, J.P., Odell, J.: Agent UML: a formalism for specifying multiagent interaction. In: Proceedings of the ICSE 2000 Workshops on Agent-Oriented Software Engineering, AOSE2000, pp. 91–103, Springer, Berlin/Heidelberg (2001)Google Scholar
  27. 27.
    de Araújo Lima, E.F., de Figueiredo, J.C.A., Guerrero, D.D.S.: Using coloured petri nets to compare mobile agent design patterns. Electron. Notes Theor. Comput. Sci. 95, 287–305 (2004)Google Scholar
  28. 28.
    Mak, J.K.H., Choy, C.S.T., Lun, D.P.K.: Precise modeling of design patterns in UML. In: Proceedings of the 26th International Conference on Software Engineering, ICSE’04, IEEE Computer Society, pp. 252–261, Scotland, UK (2004)Google Scholar
  29. 29.
    Noriega, P.: Agent mediated auctions: the fishmarket metaphor. Ph.D. thesis, Universitat Autonoma de Barcelona (1997)Google Scholar
  30. 30.
    Smith, R.G.: The contract net protocol: a high level communications and control in a distributed problem solver. IEEE Trans. Comput. C-29(12), 1104–1113 (2006)Google Scholar
  31. 31.
    Sprinkle, J.M.: Model integrated program synthesis of agent interaction protocols. Master thesis, Graduate School of Vanderbilt University (2000)Google Scholar
  32. 32.
    AISB CONVENTION[S/OL]: (2003)Google Scholar
  33. 33.
    Smallwood, R.D., Sondik, E.J.: The optimal control of partially observable Markov processes over a Nite Horizon [J]. Oper. Res. 21, 1071–1088 (1973)MATHGoogle Scholar
  34. 34.
    Bouzid, M.: Antoni Ligeza: temporal causal abduction. Constraints 5(3), 303–319 (2000)CrossRefMATHMathSciNetGoogle Scholar
  35. 35.
    Goldberg, D.: Evaluating the dynamics of agent-environment interaction. Ph.D. thesis, Institute for Robotics and Intelligent Systems, University of Southern California (2001)Google Scholar
  36. 36.
    Ashby, W.R.: An Introduction to Cybernetics. Chapman & Hall, London (1956)CrossRefMATHGoogle Scholar
  37. 37.
    Sinha, A.K., Buckley, R.A.: Equilibrium diagram of the iron-molybdenum system [J]. J. Iron Steel Inst. 205, 191 (1967)Google Scholar
  38. 38.
    Gell-Mann, M.: The Quark and the Jaguar [M]. Freeman, New York (1994)Google Scholar
  39. 39.
    Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, New York (1999)MATHGoogle Scholar
  40. 40.
    Heylighen, F.: The Evolution of Complexity. Kluwer Academic, Dordrecht (1996)Google Scholar
  41. 41.
    Eberhart, R., Shi, Y., Kennedy, J.: Swarm Intelligence. Morgan Kaufmann, San Francisco (2001)Google Scholar
  42. 42.
    Qian, X., Yu, J., Dai, R.: A new scientific field–open complex giant systems and the methodology (in Chinese). Chin. J. Nat. 13(1), 3–10 (1990)Google Scholar
  43. 43.
    Qian, X.: Revisiting issues on open complex giant systems (in Chinese). Pattern Recogn. Artif. Intell. 4(1), 5–8 (1991)Google Scholar
  44. 44.
    Qian, X.: Building Systematology (in Chinese). Shanghai Jiaotong University Press, Shanghai (2007)Google Scholar
  45. 45.
    Dai, R.: Qualitative-to-quantitative metasynthetic engineering (in Chinese). Pattern Recogn. Artif. Intell. 6(2), 60–65 (1993)Google Scholar
  46. 46.
    Cao, L., Dai, R.: Open Complex Intelligent Systems (in Chinese). Post & Telecom Press, Beijing (2008)Google Scholar
  47. 47.
    Cao, L., Zhang, C., Zhou, M.: Engineering open complex agent systems: a case study. IEEE Trans. Syst. Man Cybern. C: Appl. Rev. 38(4), 483–496 (2008)Google Scholar
  48. 48.
    Cao, L., Dai, R., Zhou, M.: Metasynthesis: M-space M-interaction and M-computing for open complex giant systems. IEEE Trans. Syst. Man Cybern. A 39(5), 1007–1021 (2009)Google Scholar
  49. 49.
    Dai, R., et al.: Metasynthesis of Intelligent Systems (in Chinese). Zhejiang Science & Technology Press, Zhejiang (1995)Google Scholar
  50. 50.
    Ferber, J.: Multi-agent Systems: An Introduction to Distributed Artificial Intelligence. Addison-Wesley/Longman, Harlow (1998)Google Scholar
  51. 51.
    Cao, L.B., Dai, R.W.: Social abstraction for agent-based open giant intelligent systems. In: Proceedings of International Conference on Intelligent Information Technology (ICIIT-02), 22–25 Sept, pp. 47–52. Beijing, China ISBN 7-115-75100-5/0267Google Scholar
  52. 52.
    Kosecka, J., Bogoni, L.: Application of discrete events systems for modeling and controlling robotic agents. In: Proceedings of the 1994 IEEE International Conference on Robotics and Automation, IEEE, pp. 2557–2562, San Diego, CA (1994)Google Scholar
  53. 53.
    Horswill, I.: Analysis of adaptation and environment. Artif. Intell. 73(1–2), 1–30 (1995)Google Scholar
  54. 54.
    Agre, P.E.: Computational research on interaction and agency. Artif. Intell. 72, 1–52 (1995)Google Scholar
  55. 55.
    Puterman, M.L.: Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley-Interscience, Hoboken (2005)Google Scholar

Copyright information

© Springer-Verlag London 2015

Authors and Affiliations

  • Longbing Cao
    • 1
  1. 1.Advanced Analytics InstituteUniversity of Technology, SydneySydneyAustralia

Personalised recommendations