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

页码：860 / 863

页数：4

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