13 citations · 19 across the 6 of their papers we have counts for
6 papers
Checkmating One, by Using Many: Combining Mixture of Experts with MCTS to Improve in Chess
Felix Helfenstein, Johannes Czech, Jannis Blüml +2
In games like chess, strategy evolves dramatically across distinct phases - the opening, middlegame, and endgame each demand different forms of reasoning and decision-making. Yet,…
Know your Enemy: Investigating Monte-Carlo Tree Search with Opponent Models in Pommerman
Jannis Weil, Johannes Czech, Tobias Meuser +1
In combination with Reinforcement Learning, Monte-Carlo Tree Search has shown to outperform human grandmasters in games such as Chess, Shogi and Go with little to no prior domain k…
Representation Matters for Mastering Chess: Improved Feature Representation in AlphaZero Outperforms Switching to Transformers
Johannes Czech, Jannis Blüml, Kristian Kersting +1
While transformers have gained recognition as a versatile tool for artificial intelligence (AI), an unexplored challenge arises in the context of chess - a classical AI benchmark.…
Generative Adversarial Neural Cellular Automata
Maximilian Otte, Quentin Delfosse, Johannes Czech +1
Motivated by the interaction between cells, the recently introduced concept of Neural Cellular Automata shows promising results in a variety of tasks. So far, this concept was most…
Monte-Carlo Graph Search for AlphaZero
Johannes Czech, Patrick Korus, Kristian Kersting
The AlphaZero algorithm has been successfully applied in a range of discrete domains, most notably board games. It utilizes a neural network, that learns a value and policy functio…
Learning to play the Chess Variant Crazyhouse above World Champion Level with Deep Neural Networks and Human Data
Johannes Czech, Moritz Willig, Alena Beyer +2
Deep neural networks have been successfully applied in learning the board games Go, chess and shogi without prior knowledge by making use of reinforcement learning. Although starti…