Checkmating One, by Using Many: Combining Mixture of Experts with MCTS to Improve in Chess. (arXiv:2401.16852v1 [cs.LG])

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 (MCTS). Our methodology employs a suite of specialized
models, each designed to respond to specific changes in the game's input data.
This results in a framework with sparsely activated models, which provides
significant computational benefits. Our framework combines the MoE method with
MCTS, in order to align it with the strategic phases of chess, thus …

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