Evolving Behaviour Trees for the Mario Bros Game Using Grammatical Evolution
Inhaltsverzeichnis
Reference
Perez, D., Nicolau, M., Michael O'Neill and Anthony Brabazon (2011) Evolving Behaviour Trees for the Mario Bros Game Using Grammatical Evolution. 3rd European event on bio-inspired algorithms in games (EvoGames 2011) Torino, Italy, , 27-APR-11 - 29-APR-11 , pp.123-132
DOI
Abstract
The 2010 Mario AI Competition is a contest organized by Julian Togelius and Sergey Karakovskiy, and it is the successor to the competition held by the same organizers in 2009 [JSR10]. The 2010 competition took place in four different events: EvoStar 2010, World Congress on Computational Intelligence (WCCI) 2010, Conference on Computational Intelligence and Games (CIG) 2010 and Games Innovation Conference (GIC) 2010. The participants of the competition are requested to submit a bot that can participate in up to three different tracks: gameplay, learning and level gener- ation. The bot presented by the authors for this competition took part in the gameplay track of the CIG’10, where the bots are evaluated in levels that have not been seen previously by the competitors.
The score of each bot is based on the distance run by Mario (the bot), plus the sum of some other factors, like collected items, enemies killed and time left. The evaluation is made over several executions, varying level length, enemy types and difficulty, so the final score is the sum of all these evaluations. The bot that gets the highest score becomes the winner of the competition.
Extended Abstract
Bibtex
Used References
[MBW] Mario AI Benchmark, http://code.google.com/p/marioai/
[MAI10] 2010 Mario AI Championship, http://www.marioai.org
[JSR10] Julian Togelius, Sergey Karakovskiy and Robin Baumgarten: The 2009 Mario AI Competition. In: IEEE Congress on Evolutionary Computation, Proceedings. pp. FIXME–FIXME IEEE Press (2010)
[CDC10] Champandard, A., Dawe, M., Cerpa, D. H.: Behavior Trees: Three Ways of Cultivating Strong AI. In: Game Developers Conference, Audio Lecture. (2010)
[Col07] Colvin, R., Hayes, I. J.: A Semantics for Behavior Trees. ARC Centre for Complex Systems, tech. report ACCS-TR-07-01. (2007)
[Dro04] Dromey, R. G.: From Requirements to Design: Formalizing the Key Steps. In: International Conference on Software Engineering and Formal Methods, Proceed- ings. (2004)
[Gol89] Goldberg, D. E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley (1989)
[Hol75] Holland, J. H.: Adaptation in Natural and Artificial Systems. University of Michigan Press (1975)
[Isl05] Isla, D.: Managing Complexity in the Halo 2 AI System. In: Game Developers Conference, Proceedings. (2005)
[Mch07] McHugh, L.: Three Approaches to Behavior Tree AI. In: Game Developers Conference, Proceedings. (2007)
[MS04] Mateas, M., Stern, A.: Managing Intermixing Behavior Hierarchies. In: Game Developers Conference, Proceedings. (2004)
[ND06] Nicolau, M., Dempsey, I.: Introducing Grammar Based Extensions for Gram- matical Evolution. In: IEEE Congress on Evolutionary Computation, Proceedings. pp. 2663–2670 IEEE Press (2006)
[OR03] O’Neill, M., Ryan, C.: Grammatical Evolution: Evolutionary Automatic Pro- gramming in a Arbitrary Language. Kluwer Academic Publishers (2003)
[RA03] Ryan, C., Azad, R.M.A.: Sensible initialisation in grammatical evolution. In: Barry, A.M. (ed.) GECCO 2003: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference. pp. 142–145. AAAI (July 2003)
Links
Full Text
http://aimario.googlecode.com/svn-history/r104/trunk/Doc/EvoGames/mario.pdf
