Achieving human-AI alignment in complex multi-agent games is crucial for
creating trustworthy AI agents that enhance gameplay. We propose a method to
evaluate this alignment using an interpretable task-sets framework, focusing on
high-level behavioral tasks instead of low-level policies. Our approach has
three components. First, we analyze extensive human gameplay data from Xbox's
Bleeding Edge (100K+ games), uncovering behavioral patterns in a complex task
space. This task space serves as a basis set for a behavior manifold capturing
interpretable axes: fight-flight, explore-exploit, and solo-multi-agent.
Second, we train an AI agent to play Bleeding Edge using a Generative
Pretrained Causal Transformer and measure its behavior. Third, we project human
and AI gameplay to the proposed behavior manifold to compare and contrast. This
allows us to interpret differences in policy as higher-level behavioral
concepts, e.g., we find that while human players exhibit variability in
fight-flight and explore-exploit behavior, AI players tend towards uniformity.
Furthermore, AI agents predominantly engage in solo play, while humans often
engage in cooperative and competitive multi-agent patterns. These stark
differences underscore the need for interpretable evaluation, design, and
integration of AI in human-aligned applications. Our study advances the
alignment discussion in AI and especially generative AI research, offering a
measurable framework for interpretable human-agent alignment in multiplayer
gaming.

评估人工智能与人类在复杂多智体游戏中的一致性，提出了一种使用可解释的任务集框架评估的方法，重点关注高级行为任务而非低级策略。通过分析人类游戏数据和训练 AI 代理，将人类和 AI 游戏对比，发现人类和 AI 之间存在显著差异，强调在多人游戏中应对人工智能的评估、设计和整合进行清晰解释。