论文标题
部分可观测时空混沌系统的无模型预测
Visualising Multiplayer Game Spaces
论文作者
论文摘要
我们将四个不同的“游戏空间”比较了它们在表征多玩家桌面游戏方面的有用性，随着玩家数量的变化，对游戏特征的任何基本变化都特别感兴趣。在每种情况下，我们都有16维的特征空间，并将其减少为二维可视化景观。 我们发现，从蒙特卡洛树搜索（MCT）中优化参数获得的空间是最直接的解释，可以从不完美的信息，对抗性对手和奖励稀疏性的相对重要性来表征我们的游戏集。这些结果与使用游戏树的属性定义的空间无关。 降低这一维度并未显示出作为玩家数量的任何一般效果。因此，我们考虑使用原始功能将游戏分为两组的问题。随着玩家数量的变化，游戏特征的变化很大，而没有这种效果的玩家。
We compare four different `game-spaces' in terms of their usefulness in characterising multi-player tabletop games, with a particular interest in any underlying change to a game's characteristics as the number of players changes. In each case we take a 16-dimensional feature space, and reduce it to a 2-dimensional visualizable landscape. We find that a space obtained from optimization of parameters in Monte Carlo Tree Search (MCTS) is the most directly interpretable to characterise our set of games in terms of the relative importance of imperfect information, adversarial opponents and reward sparsity. These results do not correlate with a space defined using attributes of the game-tree. This dimensionality reduction does not show any general effect as the number of players. We therefore consider the question using the original features to classify the games into two sets; those for which the characteristics of the game changes significantly as the number of players changes, and those for which there is no such effect.
