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A Bayesian Ensemble Regression Framework on the Angry Birds Game

机译：愤怒的小鸟游戏上的贝叶斯合奏回归框架

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摘要

In this paper, we introduce AngryBER, an intelligent agent architecture on the Angry Birds domain that employs a Bayesian ensemble inference mechanism to promote decision-making abilities. It is based on an efficient tree-like structure for encoding and representing game screenshots, where it exploits its enhanced modeling capabilities. This has the advantage to establish an informative feature space and translate the task of game playing into a regression analysis problem. A Bayesian ensemble regression framework is presented by considering that every combination of objects' material and bird type has its own regression model. We address the problem of action selection as a multiarmed bandit problem, where the upper confidence bound (UCB) strategy has been used. An efficient online learning procedure has been also developed for training the regression models. We have evaluated the proposed methodology on several game levels, and compared its performance with published results of all agents that participated in the 2013 and 2014 Angry Birds AI competitions. The superiority of the new method is readily deduced by inspecting the reported results.
机译：在本文中，我们介绍了AngryBER，这是Angry Birds域上的一种智能代理体系结构，该体系结构使用贝叶斯集成推理机制来提升决策能力。它基于有效的树状结构，用于编码和表示游戏屏幕截图，并利用其增强的建模功能。这具有建立信息特征空间并将游戏任务转换为回归分析问题的优点。通过考虑对象的材料和鸟类类型的每种组合都有其自己的回归模型，提出了贝叶斯整体回归框架。我们将行动选择问题视为多臂匪徒问题，其中已使用上限置信度（UCB）策略。还开发了一种有效的在线学习程序来训练回归模型。我们已在多个游戏级别上评估了所提出的方法，并将其性能与参加2013年和2014年Angry Birds AI竞赛的所有代理商的公布结果进行了比较。通过检查报告的结果，可以轻松推断出新方法的优越性。

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