Advertisement

The Conceptual Modelling of Dynamic Teams for Autonomous Systems

  • Rick Evertsz
  • John Thangarajah
  • Michael Papasimeon
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10650)

Abstract

The concept of a ‘team’ is key in multi-agent decision-making applications such as for combat operations and disaster management. Although there are a number of team-oriented agent programming approaches, conceptual modelling of teams is not fully addressed. In this paper we present TDF-T, an extension of the TDF agent design methodology that addresses the requirements of team oriented modelling; in particular, team hierarchies, dynamic team formation, and team coordination. These concepts encapsulate team tactical behaviour which is essential to our user community who need to build and deploy complex team-based simulation applications. We show positive results in a user study that evaluates comprehension and maintainability of TDF-T models.

Keywords

Autonomous systems Multi-agent systems Organisational modelling Team modelling 

References

  1. 1.
    Akbari, O.Z.: A survey of agent-oriented software engineering paradigm: towards its industrial acceptance. J. Comput. Eng. Res. 1(2), 14–28 (2010)Google Scholar
  2. 2.
    Akbori, F.: Autonomous-agent based simulation of anti-submarine warfare operations with the goal of protecting a high value unit. Master’s thesis (2004)Google Scholar
  3. 3.
    Bisht, S., Malhotra, A., Taneja, S.B.: Modelling and simulation of tactical team behaviour. Def. Sci. J. 57(6), 853 (2007)CrossRefGoogle Scholar
  4. 4.
    Boissier, O., Bordini, R.H., Hübner, J.F., Ricci, A., Santi, A.: Multi-agent oriented programming with JaCaMo. Sci. Comput. Program. 78(6), 747–761 (2013)CrossRefGoogle Scholar
  5. 5.
    Bratman, M.: Faces of Intention: Selected Essays on Intention and Agency. Cambridge University Press, Cambridge (1999)CrossRefGoogle Scholar
  6. 6.
    Case, D.M., DeLoach, S.A.: Obaa++: an agent architecture for participating in multiple groups. In: Proceedings of the 2014 International Conference on Autonomous Agents and Multi-agent Systems, pp. 1367–1368. International Foundation for Autonomous Agents and Multiagent Systems (2014)Google Scholar
  7. 7.
    Cohen, P.R., Levesque, H.J.: Teamwork. Nous 25(4), 487–512 (1991)CrossRefGoogle Scholar
  8. 8.
    Evertsz, R., Fletcher, M., Jones, R., Jarvis, J., Brusey, J., Dance, S.: Implementing industrial multi-agent systems using JACKTM. In: Dastani, M.M., Dix, J., El Fallah-Seghrouchni, A. (eds.) ProMAS 2003. LNCS, vol. 3067, pp. 18–48. Springer, Heidelberg (2004). doi: 10.1007/978-3-540-25936-7_2CrossRefGoogle Scholar
  9. 9.
    Evertsz, R., Thangarajah, J., Ly, T.: A BDI-based methodology for eliciting tactical decision-making expertise. In: Sarker, R., Abbass, H.A., Dunstall, S., Kilby, P., Davis, R., Young, L. (eds.) Data and Decision Sciences in Action. LNMIE, pp. 13–26. Springer, Cham (2018). doi: 10.1007/978-3-319-55914-8_2CrossRefGoogle Scholar
  10. 10.
    Evertsz, R., Thangarajah, J., Yadav, N., Ly, T.: A framework for modelling tactical decision-making in autonomous systems. J. Syst. Softw. 110(C), 222–238 (2015). doi: 10.1016/j.jss.2015.08.046CrossRefGoogle Scholar
  11. 11.
    Giachetti, R.E., Marcelli, V., Cifuentes, J., Rojas, J.A.: An agent-based simulation model of human-robot team performance in military environments. Syst. Eng. 16(1), 15–28 (2013)CrossRefGoogle Scholar
  12. 12.
    Grosz, B.J., Kraus, S.: Collaborative plans for complex group action. Artif. Intell. 86(2), 269–357 (1996)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Heaton, L.: Unified modeling language (UML): superstructure specification, v2.0. Object Management Group, Technical report (2005)Google Scholar
  14. 14.
    Horling, B., Lesser, V.: A survey of multi-agent organizational paradigms. Knowl. Eng. Rev. 19(04), 281–316 (2004)CrossRefGoogle Scholar
  15. 15.
    Isern, D., Sánchez, D., Moreno, A.: Organizational structures supported by agent-oriented methodologies. J. Syst. Softw. 84(2), 169–184 (2011)CrossRefGoogle Scholar
  16. 16.
    Kinny, D., Georgeff, M., Rao, A.: A methodology and modelling technique for systems of BDI agents. In: Van de Velde, W., Perram, J.W. (eds.) MAAMAW 1996. LNCS, vol. 1038, pp. 56–71. Springer, Heidelberg (1996). doi: 10.1007/BFb0031846CrossRefGoogle Scholar
  17. 17.
    Norling, E.: Folk psychology for human modelling: extending the BDI paradigm. In: Proceedings of AAMAS 2004, pp. 202–209. IEEE Computer Society (2004)Google Scholar
  18. 18.
    Padgham, L., Winikoff, M.: Developing Intelligent Agent Systems: A Practical Guide, vol. 1. Wiley, Hoboken (2004)CrossRefGoogle Scholar
  19. 19.
    Rao, A., Georgeff, M., et al.: BDI agents: from theory to practice. In: Proceedings of the First ICMAS (1995), pp. 312–319, San Francisco (1995)Google Scholar
  20. 20.
    Schurr, N., Maheswaran, R., Scerri, P., Tambe, M.: From STEAM to Machinetta: the evolution of a BDI teamwork model. Cognition and Multiagent Interaction: From Cognitive Modeling to Social Simulation 2004 (2006)Google Scholar
  21. 21.
    Searle, J.R.: Responses to critics of the construction of social reality. Philos. Phenomenol. Res. 57(2), 449–458 (1997)CrossRefGoogle Scholar
  22. 22.
    Wooldridge, M.: An Introduction to Multiagent Systems. Wiley, Hoboken (2008)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Rick Evertsz
    • 1
  • John Thangarajah
    • 1
  • Michael Papasimeon
    • 2
  1. 1.RMIT UniversityMelbourneAustralia
  2. 2.Defence Science and Technology GroupMelbourneAustralia

Personalised recommendations