Mixture of Public and Private Distributions in Imperfect Information Games
This work addresses a specific challenge for AI agents in card games, offering an incremental improvement over existing methods.
The paper tackles the problem of balancing private and public information use in imperfect information games like Bridge and Poker to avoid exploitation while maintaining consistency, proposing a new belief distribution that improves performance based on game position.
In imperfect information games (e.g. Bridge, Skat, Poker), one of the fundamental considerations is to infer the missing information while at the same time avoiding the disclosure of private information. Disregarding the issue of protecting private information can lead to a highly exploitable performance. Yet, excessive attention to it leads to hesitations that are no longer consistent with our private information. In our work, we show that to improve performance, one must choose whether to use a player's private information. We extend our work by proposing a new belief distribution depending on the amount of private and public information desired. We empirically demonstrate an increase in performance and, with the aim of further improving performance, the new distribution should be used according to the position in the game. Our experiments have been done on multiple benchmarks and in multiple determinization-based algorithms (PIMC and IS-MCTS).