RT Journal Article
T1 Using Self-Adaptive Evolutionary Algorithms to Evolve Dynamism-Oriented Maps for a Real Time Strategy Game
A1 Lara-Cabrera, Raúl
A1 Cotta-Porras, Carlos
A1 Fernández-Leiva, Antonio José
K1 Videojuegos
K1 Algoritmos computacionales
AB This work presents a procedural content generation system that uses an evolutionary algorithm in order to generate interesting maps for a real-time strategy game, called Planet Wars. Interestingness is here captured by the dynamism of games (i.e., the extent to which they are action-packed). We consider two different approaches to measure the dynamism of the games resulting from these generated maps, one based on fluctuations in the resources controlled by either player and another one based on their confrontations. Both approaches rely on conducting several games on the map under scrutiny using top artificial intelligence (AI) bots for the game. Statistic gathered during these games are then transferred to a fuzzy system that determines the map's level of dynamism. We use an evolutionary algorithm featuring self-adaptation of mutation parameters and variable-length chromosomes (which means maps of different sizes) to produce increasingly dynamic maps.
PB Springer-Verlag
YR 2013
FD 2013
LK http://hdl.handle.net/10630/5503
UL http://hdl.handle.net/10630/5503
LA eng
NO 9th International Conference on Large Scale Scientific Computations. The final publication is available at link.springer.com
NO TIN2011-28627-C04-01, P10-TIC-6083
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 21 ene 2026