Quality diversity (QD) is a branch of evolutionary computation that seeks high-quality and behaviorally diverse solutions to a problem. While adversarial problems are common, classical QD cannot be easily applied to them, as both the fitness and the behavior depend on the opposing solutions. Recently, Generational Adversarial MAP-Elites (GAME) has been proposed to coevolve both sides of an adversarial problem by alternating the execution of a multi-task QD algorithm against previous elites, called tasks. The original algorithm selects new tasks based on a behavioral criterion, which may lead to undesired dynamics due to inter-side dependencies. In addition, comparing sets of solutions cannot be done directly using classical QD measures due to side dependencies. In this paper, we (1) use an inter-variants tournament to compare the sets of solutions, ensuring a fair comparison, with 6 measures of quality and diversity, and (2) propose two tournament-informed task selection methods to promote higher quality and diversity at each generation. We evaluate the variants across three adversarial problems: Pong, a Cat-and-mouse game, and a Pursuers-and-evaders game. We show that the tournament-informed task selection method leads to higher adversarial quality and diversity. We hope that this work will help further advance adversarial quality diversity. Code, videos, and supplementary material are available at https://github.com/Timothee-ANNE/GAME_tournament_informed.

翻译：质量多样性（QD）是进化计算的一个分支，旨在寻找问题中高质量且行为多样化的解决方案。由于对抗性问题普遍存在，但经典QD难以直接应用于此类问题，因为适应度和行为均取决于对立解决方案。最近提出的世代对抗MAP-Elites（GAME）算法通过交替执行多任务QD算法对抗先前精英解（称为任务），实现了对抗性问题的双方案共同进化。原始算法基于行为标准选择新任务，但由于双方案间的相互依赖可能导致不良动态。此外，由于方案间依赖性，无法直接使用经典QD度量比较解集。本文（1）采用跨变体锦标赛比较解集，确保公平比较，并提出6种质量与多样性度量指标；（2）提出两种锦标赛知情任务选择方法，以提升每一代的质量与多样性。我们在三个对抗性问题（Pong游戏、猫鼠游戏、追逐-逃避游戏）上评估了各变体，证明锦标赛知情任务选择方法能带来更高的对抗质量与多样性。希望本研究能进一步推动对抗性质量多样性的发展。代码、视频及补充材料见 https://github.com/Timothee-ANNE/GAME_tournament_informed。