Evolving Teams of Cooperating Agents for Real-Time Strategy Game. Lichocki, P., Krawiec, K., Jaśkowski, W., Giacobini, M., Brabazon, A., Cagnoni, S., Caro, G.&nbsp;A<nbsp>D., Ekárt, A., Esparcia-Alcázar, A., Farooq, M., Fink, A., Machado, P., McCormack, J., ONeill, M., Neri, F., Preuss, M., Rothlauf, F., Tarantino, E., & Yang, S. In Applications of Evolutionary Computing, EvoWorkshops, volume 5484, of Lecture Notes in Computer Science, pages 333--342, 2009.
abstract   bibtex   
We apply gene expression programing to evolve a player for a real-time strategy (RTS) video game. The paper describes the game, evolutionary encoding of strategies and the technical implementation of experimental framework. In the experimental part, we compare two setups that differ with respect to the used approach of task decomposition. One of the setups turns out to be able to evolve an effective strategy, while the other leads to more sophisticated yet inferior solutions. We discuss both the quantitative results and the behavioral patterns observed in the evolved strategies.
@inproceedings{ lichocki09evolving,
  author = {Pawel Lichocki and Krzysztof Krawiec and Wojciech Jaśkowski and Mario Giacobini and Anthony Brabazon and Stefano Cagnoni and Gianni A Di Caro and Anikó Ekárt and Anna Esparcia-Alcázar and Muddassar Farooq and Andreas Fink and Penousal Machado and Jon McCormack and Michael ONeill and Ferrante Neri and Mike Preuss and Franz Rothlauf and Ernesto Tarantino and Shengxiang Yang},
  title = {Evolving Teams of Cooperating Agents for Real-Time Strategy Game},
  series = {Lecture Notes in Computer Science},
  abstract = {We apply gene expression programing to evolve a player for a real-time strategy (RTS) video game. The paper describes the game, evolutionary encoding of strategies and the technical implementation of experimental framework. In the experimental part, we compare two setups that differ with respect to the used approach of task decomposition. One of the setups turns out to be able to evolve an effective strategy, while the other leads to more sophisticated yet inferior solutions. We discuss both the quantitative results and the behavioral patterns observed in the evolved strategies.},
  booktitle = {Applications of Evolutionary Computing, EvoWorkshops},
  isbn = {978-3-642-01128-3},
  pages = {333--342},
  volume = {5484},
  year = {2009}
}

Downloads: 0