Fingerprinting Tabletop Games

被引：0
|
作者：
Goodman, James [1 ]
Perez-Liebana, Diego [1 ]
Lucas, Simon [1 ]
机构：
[1] Queen Mary Univ London, Game AI Res Grp, London, England
来源：
2021 IEEE CONFERENCE ON GAMES (COG) | 2021年
基金：
英国工程与自然科学研究理事会;
关键词：
Optimization; MCTS;
D O I：
10.1109/COG52621.2021.9619089
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We present some initial work on characterizing games using a visual 'fingerprint' generated from several independent optimisation runs over the parameters used in Monte Carlo Tree Search (MCTS). This 'fingerprint' provides a useful tool to compare games, as well as highlighting the relative sensitivity of a specific game to algorithmic variants of MCTS. The exploratory work presented here shows that in some games there is a major change in the optimal MCTS parameters when we move from 2-players to 3 or 4-players.
引用
收藏
页码：860 / 863
页数：4
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