Player modeling attempts to create a computational model which accurately approximates a player's behavior in a game. Most player modeling techniques rely on domain knowledge and are not transferable across games. Additionally, player models do not currently yield any explanatory insight about a player's cognitive processes, such as the creation and refinement of mental models. In this paper, we present our findings with using action model learning (AML), in which an action model is learned given data in the form of a play trace, to learn a player model in a domain-agnostic manner. We demonstrate the utility of this model by introducing a technique to quantitatively estimate how well a player understands the mechanics of a game. We evaluate an existing AML algorithm (FAMA) for player modeling and develop a novel algorithm called Blackout that is inspired by player cognition. We compare Blackout with FAMA using the puzzle game Sokoban and show that Blackout generates better player models.
翻译:玩家建模尝试创建计算模型, 精确地近似玩家在游戏中的行为。 大多数玩家建模技术依靠域知识, 并且不能在游戏中相互转让。 此外, 玩家建模模型目前没有产生任何关于玩家认知过程的解释性洞察力, 比如创建和完善心理模型。 在本文中, 我们通过使用动作模型学习( AML) 来展示我们的发现, 行动模型以玩耍追踪的形式获得数据, 以域不可知的方式学习玩家模型。 我们通过引入一种技术来量化地估计玩家对游戏机理学的了解程度, 来展示这个模型的实用性。 我们评估了玩家建模的现有的 AML 算法( FAMA), 并开发了由玩家认知启发的称为“ 黑” 的新算法。 我们用拼图游戏 Sokoban 来比较“ 黑点” 和 FAMA, 并显示黑点产生更好的玩家模型 。