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According to the results of self-play experiments, a selective sampling simulation-based betting strategy for Loki-2 significantly outperforms the static-evaluation based alternatives. Similar to what has been seen with brute-force search in games like chess, the effect of the simulation (search) amplifies the quality of the evaluation function, allowing high performance to be achieved without adding additional expert knowledge. Selective sampling uses the data available about the game and the opponents to increase the quality of the information obtained with each simulation run. Yet, the work on selective-sampling simulation in poker is still in its early stages. The knowledge component and selection methods have to be tuned with the algorithmic component of the simulation, and the right balance between the different simulation tradeoffs (cost per trial versus number of trials, random versus systematic approach) has to be found.

Lourdes Pena