In this article, we present an approach to generate a team of general video game playing agents with differentiated behaviors that can ultimately assist in the game development process. We consider the agent behavior as the corresponding outcomes of playing the game: rate of wins, score, exploration, enemies killed, items collected, etc. We create and identify agents that are expected to achieve particular goals but do not necessarily simulate human behavior during gameplay. We present a solution that, by heuristic diversification , provides a controller with different heuristics and a corresponding set of weights , driving its actions. Given the simplicity of this behavior-encoding and its easiness to evolve, we use Multidimensional Archive of Phenotypic Elites to generate different solutions that elicit particular behaviors and assemble a team . The resulting agents are allocated in a feature space, used to identify the expectations of each of them. We generate a team for four games of the General Video Game Artificial Intelligence framework and find six different behavior-type agents in each. We include an experiment to check the portability of these agents when playing alternative levels and an exploratory work aiming to use them to detect design flaws in game levels.

中文翻译：

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超越为赢而玩：创建一支具有独特行为的代理团队来实现自动化游戏

在本文中，我们提出了一种生成具有差异化行为的通用视频游戏代理团队，最终可以协助游戏开发过程。我们将代理行为视为玩游戏的相应结果：获胜率、得分、探索、杀死敌人、收集物品等。我们创建并识别预期实现特定目标但不一定模拟人类行为的代理。游戏玩法。我们提出了一个解决方案，通过启发式多样化，为控制器提供了不同的启发式和一组相应的重量，驱动其行动。鉴于此的简单性由于行为编码及其易于进化，我们使用表型精英的多维档案来生成不同的解决方案，引发特定的行为并组装一个团队 。生成的代理被分配在特征空间中，用于识别每个代理的期望。我们生成一个团队研究了通用视频游戏人工智能框架的四款游戏，并找到了六种不同的每个行为类型代理。我们进行了一项实验，以检查这些代理在玩替代关卡时的可移植性，以及一项旨在使用它们来检测游戏关卡中的设计缺陷的探索性工作。