Skip to main content

Organizing Scalable Adaptation in Serious Games

  • Conference paper
Book cover Agents for Educational Games and Simulations (AEGS 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7471))

Included in the following conference series:

Abstract

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.

This research has been supported by the GATE project, funded by the Netherlands Organization for Scientific Research (NWO) and the Netherlands ICT Research and Innovation Authority (ICT Regie).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 49.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  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. 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. 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)

    Chapter  Google Scholar 

  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, 145–154

    Google Scholar 

  5. Chen, J.: Flow in games. Communications of the ACM 50(4), 31–34 (2007)

    Article  Google Scholar 

  6. Dastani, M.: 2APL: A practical agent programming language. Autonomous Agents and Multi-Agent Systems 16, 214–248 (2008)

    Article  Google Scholar 

  7. Dignum, V.: A Model for Organizational Interaction: based on Agents, founded in Logic. SIKS Dissertation, series (2004)

    Google Scholar 

  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)

    Chapter  Google Scholar 

  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. 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. Lees, M., Logan, B., Theodoropoulos, G.: Agents, games and HLA. Simulation Modelling Practice and Theory 14(6), 752–767 (2006)

    Article  Google Scholar 

  12. Magerko, B., Laird, J., Assanie, M., Kerfoot, A., Stokes, D.: AI characters and directors for interactive computer games. Ann Arbor 1001, 48109–2110

    Google Scholar 

  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)

    Chapter  Google Scholar 

  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)

    Article  Google Scholar 

  15. Rabin, S.: AI Game Programming Wisdom. Charles River Media (2002)

    Google Scholar 

  16. Riedl, M., Stern, A.: Failing believably: Toward drama management with autonomous actors in interactive narratives. Technologies for Interactive Digital Storytelling and Entertainment, 195–206

    Google Scholar 

  17. Sandholm, T.: Algorithm for optimal winner determination in combinatorial auctions. Artificial Intelligence 135(1-2), 1–54 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  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. Si, M., Marsella, S., Pynadath, D.: Thespian: An architecture for interactive pedagogical drama. In: Proc. Of AIED, Citeseer (2005)

    Google Scholar 

  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)

    Article  Google Scholar 

  21. Spronck, P., Ponsen, M., Sprinkhuizen-Kuyper, I., Postma, E.: Adaptive game AI with dynamic scripting. Machine Learning 63(3), 217–248 (2006)

    Article  Google Scholar 

  22. Westra, J., Dignum, F., Dignum, V.: Modeling agent adaptation in games. In: Proceedings of OAMAS 2008 (2008)

    Google Scholar 

  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)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Westra, J., Dignum, F., Dignum, V. (2012). Organizing Scalable Adaptation in Serious Games. In: Beer, M., Brom, C., Dignum, F., Soo, VW. (eds) Agents for Educational Games and Simulations. AEGS 2011. Lecture Notes in Computer Science(), vol 7471. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32326-3_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-32326-3_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32325-6

  • Online ISBN: 978-3-642-32326-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics