论文标题
为游戏AI优化加权NTBEA
Weighting NTBEA for Game AI Optimisation
论文作者
论文摘要
N-Tuple Bandit进化算法（NTBEA）已证明在优化游戏AI中的算法参数非常有效。潜在的弱点是模型中所有组件元素的简单平均值。这项研究调查了NTBEA中使用的N键盘模型的改进，通过将这些组件的信息水平和匹配的特异性加权来加权这些组件。我们将加权功能引入模型，以获得加权 - NTBEA并在四个基准功能和两个游戏环境上进行测试。这些测试表明，香草NTBEA是测试算法中最可靠和最佳性的。此外，我们表明，鉴于迭代预算，最好执行几次独立的NTBEA运行，并利用一部分预算从这些运行中找到最佳建议。
The N-Tuple Bandit Evolutionary Algorithm (NTBEA) has proven very effective in optimising algorithm parameters in Game AI. A potential weakness is the use of a simple average of all component Tuples in the model. This study investigates a refinement to the N-Tuple model used in NTBEA by weighting these component Tuples by their level of information and specificity of match. We introduce weighting functions to the model to obtain Weighted- NTBEA and test this on four benchmark functions and two game environments. These tests show that vanilla NTBEA is the most reliable and performant of the algorithms tested. Furthermore we show that given an iteration budget it is better to execute several independent NTBEA runs, and use part of the budget to find the best recommendation from these runs.
