Learning Rules of Simplified Boardgames by Observing
Learning Rules of Simplified Boardgames by Observing
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通过观察学习简化棋盘游戏的规则
DOI：
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发表时间：
2012
期刊：
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通讯作者：
Y. Björnsson
中科院分区：
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作者：
Y. Björnsson
一般游戏（GGP）代理在只给定游戏规则的情况下，学习策略来熟练地玩各种各样的游戏。这些规则是用一种叫做游戏描述语言（Game Description language，简称GDL）的语言提供的，它规定了最初的游戏设置，什么构成了合法的移动，它们如何在游戏时更新游戏状态，游戏如何结束，以及结果是什么。在这里，我们进一步扩展了这条研究线，也就是说，我们假设玩游戏的代理必须通过观察其他人的游戏来学习游戏规则，而不是通过提供规则。我们在这里的重点将主要放在棋子移动的建模上，较少关注剩下的游戏规则属性。我们定义了一个游戏子集，我们将其命名为简化的桌面游戏，尽管它只构成了可在GDL中表达的游戏子集，但却包含了流行桌面游戏中发现的各种有趣的棋子移动模式。我们提供了一种定义良好的形式和一种实用的算法来学习简化棋盘游戏的游戏规则。我们在不同的桌面游戏和不同的观察可用性假设下，对学习算法进行了经验评估。此外，我们还表明，我们的形式至少比最先进的基于逻辑的GDL推理器提供了一个数量级的加速，以适应桌面游戏。因此，该方法与GGP系统直接相关。
General Game Playing (GGP) agents learn strategies to skillfully play a wide variety of games when given only the rules of the game. The rules are provided in a language called Game Description Language (GDL) and specify the initial game setup, what constitutes legal moves and how they update the game state when played, how the game terminates, and what the outcome is. In here we extend this line of research further, that is, we assume that the game-playing agent must learn the rules of a game by observing others play instead of them being provided. Our focus here will mainly be on modeling piece movements with less attention placed on the remaining game-rule properties. We define a subset of games, we name simplified boardgames, that despite constituting only a small subset of games expressible in GDL nonetheless encapsulate a large variety of interesting piece movement patterns found in popular boardgames. We provide a well-defined formalism and a practicable algorithm for learning game rules of simplified boardgames. We empirically evaluate the learning algorithm on different boardgames and under different assumptions of availability of observations. Furthermore, we show that our formalism offers at least an order of magnitude speedup over state-of-the-art logic-based GDL reasoners for fitting boardgames. The method is thus directly relevant for GGP systems.