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Dynamic Difficulty Adjustment for a Memory Game

  • Vladimir AraujoEmail author
  • Alejandra Gonzalez
  • Diego Mendez
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 895)

Abstract

Working memory is an important function for human cognition, it is related to some skills, such as remembering information or developing a mental calculation. Several games have been developed to train the working memory. Nevertheless, sometimes the game does not adjust adequate to users. Consequently, they end up bored by the game and leave it. This article presents a system of dynamic adjustment of the difficulty for a working memory training game, which allows generating customized levels so that the users obtain a better performance during the training of the memory. The proposed system was tested with young people, the results show that the training performance was better in comparison with a classic game and provide a better game experience to the users.

Keywords

ANFIS DDA Fuzzy Machine learning Memory game N-back Working memory training 

References

  1. 1.
    Andrade, G., Ramalho, G., Santana, H., Corruble, V.: Automatic computer game balancing: a reinforcement learning approach. In: Proceedings of the Fourth International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 2005, pp. 1111–1112. ACM, New York (2005).  https://doi.org/10.1145/1082473.1082648
  2. 2.
    Bouker, J., Scarlatos, A.: Investigating the impact on fluid intelligence by playing N-Back games with a kinesthetic modality. In: 2013 10th International Conference and Expo on Emerging Technologies for a Smarter World (CEWIT), pp. 1–3, October 2013.  https://doi.org/10.1109/CEWIT.2013.6713747
  3. 3.
    Brehmer, Y., Westerberg, H., Bckman, L.: Working-memory training in younger and older adults: training gains, transfer, and maintenance. Front. Hum. Neurosci. 6, 63 (2012).  https://doi.org/10.3389/fnhum.2012.00063CrossRefGoogle Scholar
  4. 4.
    Chacko, A., et al.: A randomized clinical trial of Cogmed Working Memory Training in school-age children with ADHD: a replication in a diverse sample using a control condition. J. Child Psychol. Psychiatry Allied Discipl. 55(3), 247–255 (2014).  https://doi.org/10.1111/jcpp.12146CrossRefGoogle Scholar
  5. 5.
    Deveau, J., Jaeggi, S.M., Zordan, V., Phung, C., Seitz, A.R.: How to build better memory training games. Front. Syst. Neurosci. 8, 243 (2015).  https://doi.org/10.3389/fnsys.2014.00243CrossRefGoogle Scholar
  6. 6.
    Gutirrez-Martnez, F., Ramos, M.: La memoria operativa como capacidad predictora del rendimiento escolar. Estudio de adaptacin de una medida de memoria operativa para nios y adolescentes. Psicologa Educativa 20(1) (Jun 2014)Google Scholar
  7. 7.
    Hardy, J.L., et al.: Enhancing cognitive abilities with comprehensive training: a large, online, randomized, active-controlled trial. PLoS ONE 10(9) (2015).  https://doi.org/10.1371/journal.pone.0134467CrossRefGoogle Scholar
  8. 8.
    Jaeggi, S., Buschkuehl, M., Jonides, J., Perrig, W.: Improving fluid intelligence with training on working memory. PNAS 105(19), 6829–6833 (2008)CrossRefGoogle Scholar
  9. 9.
    Lora, D., Sánchez-Ruiz-Granados, A.A., González-Calero, P.A., Gómez-Martín, M.A.: Dynamic difficulty adjustment in tetris. In: FLAIRS Conference (2016)Google Scholar
  10. 10.
    McDermott, A.F., Bavelier, D., Green, C.S.: Memory abilities in action video game players. Comput. Hum. Behav. 34, 69–78 (2014).  https://doi.org/10.1016/j.chb.2014.01.018CrossRefGoogle Scholar
  11. 11.
    McNab, F., et al.: Changes in cortical dopamine D1 receptor binding associated with cognitive training. Science 323(5915), 800–802 (2009).  https://doi.org/10.1126/science.1166102CrossRefGoogle Scholar
  12. 12.
    Nouchi, R., et al.: Brain training game improves executive functions and processing speed in the elderly: a randomized controlled trial. PloS One 7(1), e29676 (2012).  https://doi.org/10.1371/journal.pone.0029676CrossRefGoogle Scholar
  13. 13.
    Shaker, N., Yannakakis, G., Togelius, J.: Towards automatic personalized content generation for platform games. In: Proceedings of the Sixth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2010, Stanford, California, USA, pp. 63–68. AAAI Press (2010)Google Scholar
  14. 14.
    Sutanto, K., Suharjito, D.: Dynamic difficulty adjustment in game based on type of player with ANFIS method. J. Theor. Appl. Inf. Technol. 65(1), 254–260 (2014)Google Scholar
  15. 15.
    Watcharasatharpornpong, N., Kotrajaras, V.: Automatic level difficulty adjustment in platform games using genetic algorithm based methodology. In: International Conference and Industry Symposium on Computer Games, May 2009Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Vladimir Araujo
    • 1
    Email author
  • Alejandra Gonzalez
    • 1
  • Diego Mendez
    • 1
  1. 1.Pontificia Universidad JaverianaBogotaColombia

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