1. The 2016 Two-Player GVGAI Competition
- Author
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Simon M. Lucas, Raluca D. Gaina, Dennis J. N. J. Soemers, Diego Perez-Liebana, Tom Vodopivec, Adrien Couëtoux, Florian Kirchgesner, Mark H. M. Winands, Jialin Liu, DKE Scientific staff, and RS: FSE DACS NSO
- Subjects
Sequential game ,Computer science ,Combinatorial game theory ,GAME ,0102 computer and information sciences ,02 engineering and technology ,01 natural sciences ,General video game playing ,Strategy ,Artificial Intelligence ,0202 electrical engineering, electronic engineering, information engineering ,Electrical and Electronic Engineering ,Video game ,TREE-SEARCH ,Game mechanics ,business.industry ,Competitions ,real-time games ,ComputingMilieux_PERSONALCOMPUTING ,Screening game ,Monte Carlo tree search (MCTS) ,010201 computation theory & mathematics ,Control and Systems Engineering ,general video game playing (GVGP) ,Repeated game ,020201 artificial intelligence & image processing ,Artificial intelligence ,rolling horizon evolutionary algorithms (RHEAs) ,business ,multiplayer games ,Software - 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 toward 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 nonplaying 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.
- Published
- 2018