Measuring the level of difficulty in single player video games

Entertainment Computing

Volumn 2, Issue 4, 2011, Pages 205-213

  Aponte, Maria Virginia ^a   Levieux, Guillaume ^a   Natkin, Stephane ^a  

^a University of Paris Cité   (France)

Author keywords

Challenge; Difficulty; Evaluation; Learning; Video games

Indexed keywords

INTERACTIVE COMPUTER GRAPHICS;

CHALLENGE; DIFFICULTY; EVALUATION; LEARNING; VIDEO GAME;

HUMAN COMPUTER INTERACTION;

EID: 81455154627     PISSN: 18759521     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.entcom.2011.04.001     Document Type: Article

References (32)

1. D. Boutros, Difficulty is difficult: designing for hard modes in games, Gamasutra, 2008. Available from: (last access 01/2009). http://www.gamasutra.com/.
2. E. Adams, The designer's notebook: difficulty modes and dynamic difficulty adjustment, Gamasutra, 2008. Available from: (last access 01/2009). http://www.gamasutra.com/.
3. J. Juul, The game, the player, the world: looking for a heart of gameness, in: M. Copier, J. Raessens (Eds.), Level Up: Digital Games Research Conference Proceedings, 2003, pp. 30-45.
4. R. Hunicke, The case for dynamic difficulty adjustment in games, in: Advances in Computer Entertainment Technology, 2005, pp. 429-433.
5. Eco U. L'oeuvre ouverte 1965, Seuil.
6. Koster R. A Theory of Fun for Game Design 2005, Paraglyph Press, Scottsdale, Arizona.
7. Sweetser P., Wyeth P. Gameflow: a model for evaluating player enjoyment in games. Comput. Entertain. 2005, 3(3):3. doi:http://dx.doi.org/10.1145/1077246.1077253. URL http://dx.doi.org/10.1145/1077246.1077253.
8. Yannakakis G.N., Hallam J. Towards optimizing entertainment in computer games. Applied Artificial Intelligence 2007, 21(10):933-971.
9. M. Csikszentmihalyi, Flow: The Psychology of Optimal Experience, Harper Perennial, 1991. Available from: http://www.amazon.ca/exec/obidos/redirect?tag=citeulike04-20&path=ASIN/0060920432.
10. W. Jsselsteijn, Y. De Kort, K. Poels, A. Jurgelionis, F. Belotti, Characterising and measuring user experiences in digital games, in: International Conference on Advances in Computer Entertainment Technology, 2007.
11. G. Andrade, G. Ramalho, H. Santana, V. Corruble, Extending reinforcement learning to provide dynamic game balancing, in: IJCAI 2005 Workshop on Reasoning, Representation, and Learning in Computer Games, 2005, pp. 7-12.
12. Scott B. Architecting a game AI. AI Game Programming Wisdom 1 2002, Charles River Media, Inc.
13. Macleod A. Game design through self-play experiments. ACE '05: Proceedings of the 2005 ACM SIGCHI International Conference on Advances in Computer Entertainment Technology 2005, 421-428. ACM, New York, NY, USA, doi:http://doi.acm.org/10.1145/1178477.1178572.
14. Kirby N. AI as gameplay analysis tool. Course Technology 2008, 39-49. Cengage Learning, (Chapter 1).
15. T. Bullen, M.J. Katchabaw, N. Dyer-Witheford, Automating content analysis of video games, in: Canadian Game Studies Association (CGSA) Symposium, 2006.
16. van Lankveld G., Spronck P., Rauterberg M. Difficulty scaling through incongruity. Proceedings of the Fourth Artificial Intelligence and Interactive Digital Entertainment Conference 2008, Stanford, California, USA.
17. Nantes A., Brown R., Maire F. A framework for the semi-automatic testing of video games. Artificial Intelligence and Interactive Digital Entertainment Conference 2008, AAAI.
18. Chan B., Denzinger J., Gates D., Loose K., Buchanan J. Evolutionary behavior testing of commercial computer games. Congress on Evolutionary Computation, CEC2004 2004, 1:125-132. 10.1109/CEC.2004.1330847.
19. P. Spronck, Evolving improved opponent intelligence, in: GAME-ON Third International Conference on Intelligent Games and Simulation, 2002, pp. 94-98.
20. J.E. Laird, Game developers magazine.
21. J. Pittman, The pac-man dossier, Gamasutra, 2009. Available from: (last access 01/2009). http://www.gamasutra.com/.
22. Yannakakis G.N., Hallam J. Evolving opponents for interesting interactive computer games. Animals to Animats 8: Proceedings of the Eighth International Conference on Simulation of Adaptive Behavior (SAB-04) 2004, 499-508. The MIT Press, Santa Monica, CA, USA.
23. S.M. Lucas, Evolving a neural network location evaluator to play ms. pac-man, in: Proceedings of the 2005 IEEE Symposium on Computational Intelligence and Games (CIG05), 2005.
24. M. Gallagher, M. Ledwich, Evolving pac-man players: can we learn from raw input? in: Computational Intelligence and Games (CIG), 2007.
25. J. Bonet, C. Stauffer, Learning to play pac-man using incremental reinforcement learning, 2001. Available from: (accessed 5.12.2008). http://www.ai.mit.edu/people/stauffer/Projects/PacMan.
26. Rosca J.P. Generality versus size in genetic programming. Genetic Programming 1996: Proceedings of the First Annual Conference 1996, 381-387. MIT Press.
27. M. Gallagher, A. Ryan, Learning to play pac-man: an evolutionary, rule-based approach, in: Evolutionary Computation (CEC '03), vol. 4, 2003, pp. 2462- 2469.
28. Watkins, C.J. Learning from delayed rewards, Ph.D. Thesis, Cambridge, 1989.
29. Natkin S., Vega L. A petri net model for computer games analysis. International Journal of Intelligent Games & Simulation 2004, 3(1):37-44.
30. Natkin S., Delocque-Fourcaud A.-M., Novak E. Video Games and Interactive Media: A Glimpse at New Digital Entertainment 2006, AK Peters Ltd.
31. E. Byrne, Game Level Design (Game Development Series), Charles River Media, 2004. Available from: http://www.amazon.ca/exec/obidos/redirect?tag=citeulike09-20&path=ASIN/1584503696.
32. J. Juul, Half-Real: Video Games between Real Rules and Fictional Worlds, The MIT Press, 2005. Available from: http://www.amazon.ca/exec/obidos/redirect?tag=citeulike09-20&path=ASIN/0262101106.