Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Tom Vodopivec"'
Autor:
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
Publikováno v:
IEEE Transactions on Games, 10(2), 209-220. IEEE
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 ch
Publikováno v:
Journal of Artificial Intelligence Research. 60:881-936
Fuelled by successes in Computer Go, Monte Carlo tree search (MCTS) has achieved widespread adoption within the games community. Its links to traditional reinforcement learning (RL) methods have been outlined in the past; however, the use of RL techn
Publikováno v:
IJCAI
In this paper, we present a simple, yet effective, attention and memory mechanism that is reminiscent of Memory Networks and we demonstrate it in question-answering scenarios. Our mechanism is based on four simple premises: a) memories can be formed
Publikováno v:
IJCNN
Imbuing neural networks with memory and attention mechanisms allows for better generalisation with fewer data samples. By focusing only on the relevant parts of data, which is encoded in an internal “memory” format, the network is able to infer b
Autor:
Tom Vodopivec, Branko Šter
Publikováno v:
International Journal of Robotics and Automation. 30
Autor:
Branko Šter, Tom Vodopivec
Publikováno v:
CIG
Upper confidence bounds for trees (UCT) is one of the most popular and generally effective Monte Carlo tree search (MCTS) algorithms. However, in practice it is relatively weak when not aided by additional enhancements. Improving its performance with