Organizing Scalable Adaptation in Serious Games

  • Joost Westra
  • Frank Dignum
  • Virginia Dignum
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7471)


Serious games and other training applications have the requirement that they should be suitable for trainees with different skill levels. Current approaches either use human experts or a completely centralized approach for this adaptation. These centralized approaches become very impractical and will not scale if the complexity of the game increases. Agents can be used in serious game implementations as a means to reduce complexity and increase believability but without some centralized coordination it becomes practically impossible to follow the intended storyline of the game and select suitable difficulties for the trainee. In this paper we show that using agent organizations to coordinate the agents is scalable and allows adaptation in very complex scenarios while making sure the storyline is preserved the right difficulty level for the trainee is preserved.


Skill Level Interaction Structure Game Model Game State Combinatorial Auction 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Andrade, G., Ramalho, G., Santana, H., Corruble, V.: Extending Reinforcement Learning to Provide Dynamic Game Balancing. In: Reasoning, Representation, and Learning in Computer Games (2005)Google Scholar
  2. 2.
    Beal, C., Beck, J., Westbrook, D., Atkin, M., Cohen, P.: Intelligent modeling of the user in interactive entertainment. In: AAAI Spring Symposium on Artificial Intelligence and Interactive Entertainment, Stanford, CA (2002)Google Scholar
  3. 3.
    Brusk, J., Lager, T., Hjalmarsson, A., Wik, P.: Deal: dialogue management in scxml for believable game characters. In: Future Play 2007: Proceedings of the 2007 Conference on Future Play, pp. 137–144. ACM, New York (2007)CrossRefGoogle Scholar
  4. 4.
    Cavazza, M., Charles, F., Mead, S.: Characters in search of an author: AI-based virtual storytelling. Virtual Storytelling Using Virtual Reality Technologies for Storytelling, 145–154Google Scholar
  5. 5.
    Chen, J.: Flow in games. Communications of the ACM 50(4), 31–34 (2007)CrossRefGoogle Scholar
  6. 6.
    Dastani, M.: 2APL: A practical agent programming language. Autonomous Agents and Multi-Agent Systems 16, 214–248 (2008)CrossRefGoogle Scholar
  7. 7.
    Dignum, V.: A Model for Organizational Interaction: based on Agents, founded in Logic. SIKS Dissertation, series (2004)Google Scholar
  8. 8.
    Hübner, J.F., Sichman, J.S., Boissier, O.: S − Moise  + : A Middleware for Developing Organised Multi-agent System. In: Boissier, O., Padget, J., Dignum, V., Lindemann, G., Matson, E., Ossowski, S., Sichman, J.S., Vázquez-Salceda, J. (eds.) ANIREM and OOOP 2005. LNCS (LNAI), vol. 3913, pp. 64–78. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  9. 9.
    Hunicke, R., Chapman, V.: AI for Dynamic Difficulty Adjustment in Games. In: Proceedings of the Challenges in Game AI Workshop, Nineteenth National Conference on Artificial Intelligence, AAAI 2004 (2004)Google Scholar
  10. 10.
    Hunicke, R., Chapman, V.: AI for dynamic difficulty adjustment in games. In: Challenges in Game Artificial Intelligence AAAI Workshop, pp. 91–96 (2004)Google Scholar
  11. 11.
    Lees, M., Logan, B., Theodoropoulos, G.: Agents, games and HLA. Simulation Modelling Practice and Theory 14(6), 752–767 (2006)CrossRefGoogle Scholar
  12. 12.
    Magerko, B., Laird, J., Assanie, M., Kerfoot, A., Stokes, D.: AI characters and directors for interactive computer games. Ann Arbor 1001, 48109–2110Google Scholar
  13. 13.
    Moffat, D.: Personality Parameters and Programs. In: Petta, P., Trappl, R. (eds.) Creating Personalities for Synthetic Actors. LNCS, vol. 1195, pp. 120–165. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  14. 14.
    Panait, L., Luke, S.: Cooperative multi-agent learning: The state of the art. Autonomous Agents and Multi-Agent Systems 11(3), 387–434 (2005)CrossRefGoogle Scholar
  15. 15.
    Rabin, S.: AI Game Programming Wisdom. Charles River Media (2002)Google Scholar
  16. 16.
    Riedl, M., Stern, A.: Failing believably: Toward drama management with autonomous actors in interactive narratives. Technologies for Interactive Digital Storytelling and Entertainment, 195–206Google Scholar
  17. 17.
    Sandholm, T.: Algorithm for optimal winner determination in combinatorial auctions. Artificial Intelligence 135(1-2), 1–54 (2002)MathSciNetzbMATHCrossRefGoogle Scholar
  18. 18.
    Schurr, N., Marecki, J., Lewis, J.P., Tambe, M., Scerri, P.: The DEFACTO system: Training tool for incident commanders. In: Veloso, M.M., Kambhampati, S. (eds.) AAAI, pp. 1555–1562. AAAI Press / The MIT Press (2005)Google Scholar
  19. 19.
    Si, M., Marsella, S., Pynadath, D.: Thespian: An architecture for interactive pedagogical drama. In: Proc. Of AIED, Citeseer (2005)Google Scholar
  20. 20.
    Silverman, B., Bharathy, G., O’Brien, K., Cornwell, J.: Human behavior models for agents in simulators and games: part II: gamebot engineering with PMFserv. Presence: Teleoperators and Virtual Environments 15(2), 163–185 (2006)CrossRefGoogle Scholar
  21. 21.
    Spronck, P., Ponsen, M., Sprinkhuizen-Kuyper, I., Postma, E.: Adaptive game AI with dynamic scripting. Machine Learning 63(3), 217–248 (2006)CrossRefGoogle Scholar
  22. 22.
    Westra, J., Dignum, F., Dignum, V.: Modeling agent adaptation in games. In: Proceedings of OAMAS 2008 (2008)Google Scholar
  23. 23.
    Westra, J., van Hasselt, H., Dignum, F., Dignum, V.: Adaptive Serious Games Using Agent Organizations. In: Dignum, F., Bradshaw, J., Silverman, B., van Doesburg, W. (eds.) Agents for Games and Simulations. LNCS, vol. 5920, pp. 206–220. Springer, Heidelberg (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Joost Westra
    • 1
  • Frank Dignum
    • 1
  • Virginia Dignum
    • 2
  1. 1.Universiteit UtrechtThe Netherlands
  2. 2.Delft University of TechnologyThe Netherlands

Personalised recommendations