Advertisement

Towards a Better Understanding of Chess Players’ Personalities: A Study Using Virtual Chess Players

  • Khaldoon DhouEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10903)

Abstract

Virtual humans emerged as a topic of research in HCI and they have been used for various purposes. This paper explores the behavior of chess players in a virtual chess environment to gain more understanding about chess personalities. In particular, the focus of this research is investigating attack and defense strategies used by virtual chess grandmasters against different virtual class-B personalities who vary in their strength in the different stages of a game. These attack and defense strategies have attracted much attention in the chess community and are considered among the main aspects to chess players. They occur in different phases of the game: opening, middle game and endgame. The researcher examines virtual chess players to understand the psychology of competition between two grandmasters (attacker, defender) and three class-B chess players with different personalities: (a) strong at openings; (b) weak at openings, but strong at endgames and (c) balanced player. The virtual humans in this research represent personalities of real players. The empirical players’ results showed that the personalities could influence the error and the number of moves of the game for both grandmasters and class-B players. Such findings can be used in designing virtual chess players.

Keywords

Virtual humans Chess Attack Defense Personality 

References

  1. 1.
    Berman, N.B., Durning, S.J., Fischer, M.R., Huwendiek, S., Triola, M.M.: The role for virtual patients in the future of medical education. Acad. Med. 91(9), 1217–1222 (2016)CrossRefGoogle Scholar
  2. 2.
    Botvinnik, M.: A mathematical representation of chess. In: Botvinnik, M. (ed.) Computers. Chess and Long-Range Planning, pp. 11–25. Springer, New York (1970).  https://doi.org/10.1007/978-1-4684-6245-6_3CrossRefzbMATHGoogle Scholar
  3. 3.
    Burgoyne, A.P., Sala, G., Gobet, F., Macnamara, B.N., Campitelli, G., Hambrick, D.Z.: The relationship between cognitive ability and chess skill: a comprehensive meta-analysis. Intelligence 59, 72–83 (2016)CrossRefGoogle Scholar
  4. 4.
    Butts, S.: Virtaul kasparov, April 2002. http://www.ign.com/articles/2002/04/19/virtual-kasparov. Accessed 20 Mar 2017
  5. 5.
    Calderwood, R., Klein, G.A., Crandall, B.W.: Time pressure, skill, and move quality in chess. Am. J. Psychol. 101, 481–493 (1988)CrossRefGoogle Scholar
  6. 6.
    Chase, W.G., Simon, H.A.: The mind’s eye in chess (1973)CrossRefGoogle Scholar
  7. 7.
    Chess.com: Chessmaster 9000 questions. https://www.chess.com/forum/view/general/chessmaster9000questions?page=2. Accessed 1 Nov 2015
  8. 8.
    Cowley, M.B., Byrne, R.M.: Chess masters’ hypothesis testing. In: Proceedings of International Peer Reviewed Conference (2004)Google Scholar
  9. 9.
    De Groot, A.D.: Thought and Choice in Chess, vol. 4. Walter de Gruyter GmbH & Co KG, Berlin (1978)Google Scholar
  10. 10.
    DeVault, D., Artstein, R., Benn, G., Dey, T., Fast, E., Gainer, A., Georgila, K., Gratch, J., Hartholt, A., Lhommet, M., et al.: Simsensei kiosk: a virtual human interviewer for healthcare decision support. In: Proceedings of the 2014 International Conference on Autonomous Agents and Multi-agent Systems, pp. 1061–1068. International Foundation for Autonomous Agents and Multiagent Systems (2014)Google Scholar
  11. 11.
    Elo, A.E.: The Rating of Chessplayers, Past and Present. Arco Pub., New York (1978)Google Scholar
  12. 12.
    USCF Federation: USCF ratings distribution charts, October 2015. http://archive.uschess.org/ratings/ratedist.php. Accessed Oct 2015
  13. 13.
    Gobet, F., Jansen, P.J.: Training in chess: a scientific approach. Education and Chess (2006)Google Scholar
  14. 14.
    Gobet, F., Simon, H.A.: Templates in chess memory: a mechanism for recalling several boards. Cogn. Psychol. 31, 1–40 (1996)CrossRefGoogle Scholar
  15. 15.
    Goldin, S.E.: Recognition memory for chess positions: some preliminary research. Am. J. Psychol. 92, 19–31 (1979)CrossRefGoogle Scholar
  16. 16.
    Kenny, P., Parsons, T., Gratch, J., Rizzo, A.: Virtual humans for assisted health care. In: Proceedings of the 1st international conference on PErvasive Technologies Related to Assistive Environments, p. 6. ACM (2008)Google Scholar
  17. 17.
    Kovács, G., Ruttkay, Z., Fazekas, A.: Virtual chess player with emotions. In: 4th Hungarian Conference on Computer Graphics and Geometry (2007)Google Scholar
  18. 18.
    Lane, D.M., Chang, Y.H.A.: Chess knowledge predicts chess memory even after controlling for chess experience: evidence for the role of high-level processes. Mem. Cogn. 46, 337–348 (2017)CrossRefGoogle Scholar
  19. 19.
    Levene, M., Bar-Ilan, J.: Comparing typical opening move choices made by humans and chess engines. Comput. J. 50(5), 567–573 (2007)CrossRefGoogle Scholar
  20. 20.
    Lucas, G.M., Gratch, J., King, A., Morency, L.P.: It’s only a computer: virtual humans increase willingness to disclose. Comput. Hum. Behav. 37, 94–100 (2014)CrossRefGoogle Scholar
  21. 21.
    Newborn, M.: Kasparov Versus Deep Blue: Computer Chess Comes of Age. Springer, New York (1997).  https://doi.org/10.1007/978-1-4612-2260-6CrossRefGoogle Scholar
  22. 22.
    Powell, J.L., Grossi, D., Corcoran, R., Gobet, F., Garcia-Finana, M.: The neural correlates of theory of mind and their role during empathy and the game of chess: a functional magnetic resonance imaging study. Neuroscience 355, 149–160 (2017)CrossRefGoogle Scholar
  23. 23.
    Rasskin-Gutman, D.: Chess Metaphors: Artificial Intelligence and the Human Mind. MIT Press, Cambridge (2009)Google Scholar
  24. 24.
    Rizzo, A., Lucas, G., Gratch, J., Stratou, G., Morency, L.P., Chavez, K., Shilling, R., Scherer, S.: Automatic behavior analysis during a clinical interview with a virtual human. Stud. Health Technol. Inform. 220, 316 (2016)Google Scholar
  25. 25.
    Saariluoma, P.: Chess players’ intake of task-relevant cues. Mem. Cogn. 13(5), 385–391 (1985)CrossRefGoogle Scholar
  26. 26.
    Schlickum, M.K., Hedman, L., Enochsson, L., Kjellin, A., Felländer-Tsai, L.: Systematic video game training in surgical novices improves performance in virtual reality endoscopic surgical simulators: a prospective randomized study. World J. Surg. 33(11), 2360–2367 (2009)CrossRefGoogle Scholar
  27. 27.
    Sheridan, H., Reingold, E.M.: Chess players’ eye movements reveal rapid recognition of complex visual patterns: evidence from a chess-related visual search task. J. Vis. 17(3), 4–4 (2017)CrossRefGoogle Scholar
  28. 28.
    Swartout, W.R.: Virtual humans as centaurs: melding real and virtual. In: Lackey, S., Shumaker, R. (eds.) VAMR 2016. LNCS, vol. 9740, pp. 356–359. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-39907-2_34CrossRefGoogle Scholar
  29. 29.
    Thomas, J.C., Richards, J.T.: Achieving psychological simplicity: measures and methods to reduce cognitive complexity. In: Human-Computer Interaction: Design Issues, Solutions, and Applications, vol. 161 (2009)Google Scholar
  30. 30.
    UBISoft: Chessmaster grandmaster edition. http://chessmaster.uk.ubi.com/xi/index.php
  31. 31.
    Wise, D.M., Rosqvist, J.: Explanatory style and well-being. In: Thomas, J.C., Segal, D.L., Hersen, M. (eds.) Comprehensive Handbook of Personality and Psychopathology, p. 285. Wiley, Hoboken (2006)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Mathematics and Computer ScienceUniversity of Missouri – St. LouisSt. LouisUSA

Personalised recommendations