在线学习和挖掘人类在复杂游戏中的玩法

M. Dobre, A. Lascarides
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引用次数: 11

摘要

我们提出了一个混合模型,用于自动获取复杂游戏的策略,该模型将在线学习与从人类游戏语料库中挖掘知识相结合。我们的假设是,通过将(在线)探索与人类行为偏见相结合来学习策略的玩家将比任何只使用一种知识来源的代理表现得更好。在游戏过程中,智能体从语料库中提取玩家在类似情况下的类似动作,并通过从当前状态执行模拟来近似它们与其他可能选项的效用。我们在玩复杂输赢棋盘游戏《卡坦岛》的智能体中执行和评估我们的模型,这款游戏缺乏挑战人类专家的执行。初步实验的结果说明了这种联合模型的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Online learning and mining human play in complex games
We propose a hybrid model for automatically acquiring a policy for a complex game, which combines online learning with mining knowledge from a corpus of human game play. Our hypothesis is that a player that learns its policies by combining (online) exploration with biases towards human behaviour that's attested in a corpus of humans playing the game will outperform any agent that uses only one of the knowledge sources. During game play, the agent extracts similar moves made by players in the corpus in similar situations, and approximates their utility alongside other possible options by performing simulations from its current state. We implement and assess our model in an agent playing the complex win-lose board game Settlers of Catan, which lacks an implementation that would challenge a human expert. The results from the preliminary set of experiments illustrate the potential of such a joint model.
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