Acquisition of chess knowledge in alphazero

T McGrath, A Kapishnikov, N Tomašev… - Proceedings of the …, 2022 - National Acad Sciences
We analyze the knowledge acquired by AlphaZero, a neural network engine that learns
chess solely by playing against itself yet becomes capable of outperforming human chess …

From Analog to Digital Computing: Is Homo sapiens' Brain on Its Way to Become a Turing Machine?

A Danchin, AA Fenton - Frontiers in Ecology and Evolution, 2022 - frontiersin.org
The abstract basis of modern computation is the formal description of a finite state machine,
the Universal Turing Machine, based on manipulation of integers and logic symbols. In this …

AlphaZe∗∗: AlphaZero-like baselines for imperfect information games are surprisingly strong

J Blüml, J Czech, K Kersting - Frontiers in artificial intelligence, 2023 - frontiersin.org
In recent years, deep neural networks for strategy games have made significant progress.
AlphaZero-like frameworks which combine Monte-Carlo tree search with reinforcement …

Improving alphazero using monte-carlo graph search

J Czech, P Korus, K Kersting - Proceedings of the International …, 2021 - ojs.aaai.org
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 function …

Monte-Carlo graph search for AlphaZero

J Czech, P Korus, K Kersting - arXiv preprint arXiv:2012.11045, 2020 - arxiv.org
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 …

From Images to Connections: Can DQN with GNNs learn the Strategic Game of Hex?

Y Keller, J Blüml, G Sudhakaran, K Kersting - arXiv preprint arXiv …, 2023 - arxiv.org
The gameplay of strategic board games such as chess, Go and Hex is often characterized
by combinatorial, relational structures--capturing distinct interactions and non-local patterns …

Checkmating One, by Using Many: Combining Mixture of Experts with MCTS to Improve in Chess

F Helfenstein, J Blüml, J Czech, K Kersting - arXiv preprint arXiv …, 2024 - arxiv.org
This paper presents a new approach that integrates deep learning with computational
chess, using both the Mixture of Experts (MoE) method and Monte-Carlo Tree Search …

HiveMind: Learning to Play the Cooperative Chess Variant Bughouse with DNNs and MCTS

B Woo, P Sweetser, M Aitchison - 2023 IEEE Conference on …, 2023 - ieeexplore.ieee.org
In 2017, the AlphaZero algorithm achieved superhuman performance in chess,
outperforming the then-reigning chess engine Stockfish 8. AlphaZero has since been …

Know your Enemy: Investigating Monte-Carlo Tree Search with Opponent Models in Pommerman

J Weil, J Czech, T Meuser, K Kersting - arXiv preprint arXiv:2305.13206, 2023 - arxiv.org
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 …

Enhancing Pokémon VGC Player Performance: Intelligent Agents Through Deep Reinforcement Learning and Neuroevolution

G Rodriguez, E Villanueva, J Baldeón - International Conference on …, 2024 - Springer
Competitive Pokémon battles demand deep strategic thinking and real-time decision-
making, posing challenges for players seeking to optimize their performance. This paper …