This article provides an overview of the recently proposed forward model approximation framework for learning games of the general video game artificial intelligence (GVGAI) framework. In contrast to other general game-playing algorithms, the proposed agent model does not need a full description of the game but can learn the game's rules by observing game state transitions. Based on hierarchical knowledge bases, the forward model can be learned and revised during game-play, improving the accuracy of the agent's state predictions over time. This allows the application of simulation-based search algorithms and belief revision techniques to previously unknown settings. We show that the proposed framework is able to quickly learn a model for dynamic environments in the context of the GVGAI framework.

中文翻译：

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前向模型学习的异常容忍分层知识库

本文概述了最近提出的用于学习游戏的通用视频游戏人工智能（GVGAI）框架的前向模型近似框架。与其他一般游戏算法相比，所提出的代理模型不需要对游戏进行完整描述，但可以通过观察游戏状态转换来学习游戏规则。基于分层知识库，可以在游戏过程中学习和修改前向模型，随着时间的推移提高代理状态预测的准确性。这允许将基于模拟的搜索算法和信念修正技术应用于以前未知的设置。我们表明，所提出的框架能够在 GVGAI 框架的上下文中快速学习动态环境模型。