标题： 通用游戏博弈中最佳智能体识别 显示英文标题

标题： Best Agent Identification for General Game Playing

摘要： 我们提出了一种高效且通用的程序，以准确识别多问题领域中每个子任务的最佳算法。 我们的方法将此视为多臂老虎机的一组最佳臂识别问题，其中每个老虎机对应一个特定任务，每根臂对应一个特定算法或智能体。 我们提出了一种基于威尔逊置信区间（乐观-WS）的乐观选择过程，该过程根据其潜在遗憾减少量对所有老虎机中的每根臂进行排序。 我们在两个最受欢迎的通用游戏领域上评估了乐观-WS的性能，即通用视频游戏AI（GVGAI）框架和Ludii通用游戏系统，目标是在有限的试验次数内识别出每个游戏中的最高性能智能体。 与之前针对多臂老虎机的最佳臂识别算法相比，我们的结果在平均简单遗憾方面表现出显著的性能提升。 这种新方法可以用于显著提高通用游戏框架以及其他高算法运行时间的多任务领域的智能体评估流程的质量和准确性。 显示英文摘要

摘要： We present an efficient and generalised procedure to accurately identify the best performing algorithm for each sub-task in a multi-problem domain. Our approach treats this as a set of best arm identification problems for multi-armed bandits, where each bandit corresponds to a specific task and each arm corresponds to a specific algorithm or agent. We propose an optimistic selection process based on the Wilson score interval (Optimistic-WS) that ranks each arm across all bandits in terms of their potential regret reduction. We evaluate the performance of Optimistic-WS on two of the most popular general game domains, the General Video Game AI (GVGAI) framework and the Ludii general game playing system, with the goal of identifying the highest performing agent for each game within a limited number of trials. Compared to previous best arm identification algorithms for multi-armed bandits, our results demonstrate a substantial performance improvement in terms of average simple regret. This novel approach can be used to significantly improve the quality and accuracy of agent evaluation procedures for general game frameworks, as well as other multi-task domains with high algorithm runtimes.