Maia4All models individual chess players' move choices from as few as 20 games by enriching a population-level model with prototype players and then initializing personal embeddings via prototype matching.
We follow the notations from Maia-2 (Tang et al., 2024)
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Learning to Imitate with Less: Efficient Individual Behavior Modeling in Chess
Maia4All models individual chess players' move choices from as few as 20 games by enriching a population-level model with prototype players and then initializing personal embeddings via prototype matching.