Evolving Personalized Content for Super Mario Bros Using Grammatical Evolution
Inhaltsverzeichnis
Reference
Shaker N., Yannakakis G.N., Togelius J., Nicolau M., Michael O'Neill (2012) Evolving Personalized Content for Super Mario Bros Using Grammatical Evolution. Eighth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE-12)
DOI
Abstract
Adapting game content to a particular player’s needs and ex- pertise constitutes an important aspect in game design. Most research in this direction has focused on adapting game dif- ficulty to keep the player engaged in the game. Dynamic difficulty adjustment, however, focuses on one aspect of the gameplay experience by adjusting the content to increase or decrease perceived challenge. In this paper, we introduce a method for automatic level generation for the platform game Super Mario Bros using grammatical evolution. The gram- matical evolution-based level generator is used to generate player-adapted content by employing an adaptation mecha- nism as a fitness function in grammatical evolution to opti- mize the player experience of three emotional states: engage- ment, frustration and challenge. The fitness functions used are models of player experience constructed in our previous work from crowd-sourced gameplay data collected from over 1500 game sessions.
Extended Abstract
Bibtex
Used References
Browne, C., and Maire, F. 2010. Evolutionary game design. IEEE Transactions on Computational Intelligence and AI in Games, 2(1):1–16.
Cardamone, L.; Loiacono, D.; and Lanzi, P. 2011. Interactive evolution for the procedural generation of tracks in a high-end rac- ing game. In Genetic and Evolutionary Computation Conference, GECCO, 12–16.
Cook, M., and Colton, S. 2011. Multi-faceted evolution of simple arcade games. In IEEE Conference on Computational Intelligence and Games (CIG), 289–296.
Hastings, E. J.; Guha, R. K.; and Stanley, K. O. 2009. Evolv- ing content in the galactic arms race video game. In Proceedings of the 5th international conference on Computational Intelligence and Games, CIG’09, 241–248. Piscataway, NJ, USA: IEEE Press.
Iida, H.; Takeshita, N.; and Yoshimura, J. 2002. A metric for entertainment of boardgames: its implication for evolution of chess variants. In IWEC, 65–72.
Olesen, J.; Yannakakis, G.; and Hallam, J. 2008. Real-time chal- lenge balance in an rts game using rtneat. In IEEE Symposium On Computational Intelligence and Games, 2008, 87–94. IEEE.
O’Neill, M., and Ryan, C. 2001. Grammatical evolution. IEEE Transactions on Evolutionary Computation 5(4):349–358.
O’Neill, M.; Hemberg, E.; Gilligan, C.; Bartley, E.; McDermott, J.; and Brabazon, A. 2008. Geva: grammatical evolution in java. ACM SIGEVOlution 3(2):17–22.
Shaker, N.; Nicolau, M.; Yannakakis, G.; Togelius, J.; and O’Neill, M. 2012. Evolving Levels for Super Mario Bros Using Grammati- cal Evolution. In IEEE Transactions on Computational Intelligence and Games (CIG).
Shaker, N.; Yannakakis, G. N.; and Togelius, J. 2010. Towards Automatic Personalized Content Generation for Platform Games. In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE). AAAI Press.
Shaker, N.; Yannakakis, G. N.; and Togelius, J. 2011. Feature Analysis for Modeling Game Content Quality. In IEEE Transac- tions on Computational Intelligence and Games (CIG), 126–133.
Shaker, N.; Yannakakis, G. N.; and Togelius, J. 2012. Digging deeper into platform game level design: session size and sequen- tial features. In Proceedings of the European Conference on Appli- cations of Evolutionary Computation (EvoApplications). Springer LNCS.
Smith, A. M., and Mateas, M. 2010. Variations Forever: Flexi- bly Generating Rulesets from a Sculptable Design Space of Mini- Games. IEEE Transactions on Computational Intelligence and AI in Games.
Smith, G.; Whitehead, J.; and Mateas, M. 2010. Tanagra: A mixed-initiative level design tool. In Proceedings of the Interna- tional Conference on the Foundations of Digital Games, 209–216. ACM.
Sorenson, N.; Pasquier, P.; and DiPaola, S. 2011. A generic ap- proach to challenge modeling for the procedural creation of video game levels. IEEE Transactions on Computational Intelligence and AI in Games 3(3):229–244.
Togelius, J.; Karakovskiy, S.; and Baumgarten, R. 2010. The 2009 mario ai competition. In IEEE Congress on Evolutionary Compu- tation (CEC), 1–8. IEEE.
Togelius, J.; Preuss, M.; and Yannakakis, G. 2010. Towards mul- tiobjective procedural map generation. In Proceedings of the 2010 Workshop on Procedural Content Generation in Games, 3. ACM.
Yannakakis, G., and Togelius, J. 2011. Experience-driven procedu- ral content generation. IEEE Transactions on Affective Computing 2(3):147–161.
Links
Full Text
http://noorshaker.com/docs/PCGGE.pdf
