将人类技能分解为可解释的多维表示，提升行为预测与教练效果。
Disentangled Skill Representations for Predictive Human Modeling
- 通过反事实子技能替换实现技能解耦，构建稳定可解释的技能嵌入。
- 在赛车和棒球数据上表现优于基线，提升行为预测准确率与教练效果。
- 适合需要理解人类能力的协作型AI系统，如智能教练或辅助决策。
理解人类技能对协作、指导或辅助类AI系统至关重要。与依赖单次观测的典型潜在变量估计不同，技能是持久、组合性且行为基础的构造，需从长期模式中推断。我们提出SAIL（Skill Abstraction with Interpretable Latents），一种从自然行为中推断可解释、多维度人类技能的方法。该方法生成对瞬时表现波动鲁棒的技能嵌入，并学习可迁移的人类子技能表示。SAIL支持基于技能的行为预测，在多种域内情境中具有良好泛化能力。每个个体用一个持久的技能嵌入表征，控制专家与新手基底的混合，通过反事实子技能替换训练实现解耦。这一设计促使表示既对性能变化鲁棒，又具备结构可解释性。在赛车和棒球任务中，SAIL均展现出优异预测性能，显著优于对比基线，并持续提升行为基础的解耦效果，同时改善下游AI教练性能。
原文摘要 · Abstract (English)
Understanding human skill is important for AI systems that collaborate with, coach, or assist people. Unlike typical latent variable estimation problems which rely on single observations, skill is a persistent, compositional, and behaviorally grounded construct that must be inferred from patterns over time. We introduce Skill Abstraction with Interpretable Latents (SAIL), a method for modeling human skill as an interpretable, multi-dimensional construct inferred from naturalistic behavior. Our approach produces a skill embedding that is robust to transient performance fluctuations and learns a transferable representation of human subskills. Furthermore, SAIL supports skill-informed behavior prediction that generalizes across a variety of in-domain contexts. We represent each individual with a persistent skill embedding that controls a blend between expert and novice bases and is trained using counterfactual subskill swaps for disentanglement. This design encourages representations that are both robust to performance variation and structured for interpretability. We demonstrate across racing and baseball that SAIL achieves strong predictive performance and consistently improves behaviorally grounded disentanglement over the evaluated baselines, while also improving downstream AI coaching performance.
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