Gaina, Raluca D. and Couetoux, Adrien and Soemers, Dennis J.N.J. and Winands, Mark H.M. and Vodopivec, Tom and Kirchgessner, Florian and Liu, Jialin and Lucas, Simon M. and Perez-Liebana, Diego
This paper showcases the setting and results of the first Two-Player General Video Game AI competition, which ran in 2016 at the IEEE World Congress on Computational Intelligence and the IEEE Conference on Computational Intelligence and Games. The challenges for the general game AI agents are expanded in this track from the single-player version, looking at direct player interaction in both competitive and cooperative environments of various types and degrees of difficulty. The focus is on the agents not only handling multiple problems, but also having to account for another intelligent entity in the game, who is expected to work towards their own goals (winning the game). This other player will possibly interact with first agent in a more engaging way than the environment or any non-playing character may do. The top competition entries are analyzed in detail and the performance of all agents is compared across the four sets of games. The results validate the competition system in assessing generality, as well as showing Monte Carlo Tree Search continuing to dominate by winning the overall Championship. However, this approach is closely followed by Rolling Horizon Evolutionary Algorithms, employed by the winner of the second leg of the contest.
URL: http://ieeexplore.ieee.org/document/8100955/
Github: https://github.com/GAIGResearch/GVGAI
DOI:10.1109/TCIAIG.2017.2771241
Cite this work
@article{gaina2018gvgai2P, author= {Gaina, Raluca D. and Couetoux, Adrien and Soemers, Dennis J.N.J. and Winands, Mark H.M. and Vodopivec, Tom and Kirchgessner, Florian and Liu, Jialin and Lucas, Simon M. and Perez-Liebana, Diego}, title= {{The 2016 Two-Player GVGAI Competition}}, year= {2018}, journal= {{IEEE Transactions on Games}}, month= {June}, volume= {10}, number= {2}, pages= {209--220}, keywords= {artificial intelligence;computer games;evolutionary computation;Monte Carlo methods;tree searching;Two-Player GVGAI Competition;Two-Player General Video Game AI Competition;IEEE World Congress;Computational Intelligence;IEEE Conference;general game AI agents;single-player version;direct player interaction;competitive environments;cooperative environments;competition entries;competition system;Games;Artificial intelligence;Real-time systems;Electronic mail;Monte Carlo methods;Computer science;Evolutionary computation;Competitions;general video game playing (GVGP);Monte Carlo tree search (MCTS);multiplayer games;real-time games;rolling horizon evolutionary algorithms (RHEAs)}, url= {http://ieeexplore.ieee.org/document/8100955/}, doi= {10.1109/TCIAIG.2017.2771241}, abstract= {This paper showcases the setting and results of the first Two-Player General Video Game AI competition, which ran in 2016 at the IEEE World Congress on Computational Intelligence and the IEEE Conference on Computational Intelligence and Games. The challenges for the general game AI agents are expanded in this track from the single-player version, looking at direct player interaction in both competitive and cooperative environments of various types and degrees of difficulty. The focus is on the agents not only handling multiple problems, but also having to account for another intelligent entity in the game, who is expected to work towards their own goals (winning the game). This other player will possibly interact with first agent in a more engaging way than the environment or any non-playing character may do. The top competition entries are analyzed in detail and the performance of all agents is compared across the four sets of games. The results validate the competition system in assessing generality, as well as showing Monte Carlo Tree Search continuing to dominate by winning the overall Championship. However, this approach is closely followed by Rolling Horizon Evolutionary Algorithms, employed by the winner of the second leg of the contest.},
}