STAND让AI在少量指令下自动学习任务规则，并实时反馈训练进度。
STAND: Self-Aware Precondition Induction for Interactive Task Learning
- 基于自知能力，动态生成规则前提条件，适合小样本交互式学习。
- 在少量数据下优于XGBoost等方法，且错误复发率低，性能提升更稳定。
- 适合需要人类指导的场景，帮助用户判断何时停止训练、哪里需加强。
在交互式任务学习（ITL）中，人工智能代理通过任务执行期间的有限人类指令学习新能力。本文提出STAND，一种专为人类参与训练设计的数据高效规则前提诱导方法。其核心特征是具备自我学习认知能力，可向用户提供准确的训练进展度量。在小样本前提诱导任务中，STAND在性能上超越XGBoost、决策树、随机森林和版本空间等主流方法，且对保留样本上的性能提升具有高度准确性。评估显示，与其他模型相比，STAND展现出更平滑的单调提升趋势，错误复发率更低。这些特性提升了训练一致性，使人类指导者能判断训练是否完成，并通过识别难点区域实现主动学习支持。
原文摘要 · Abstract (English)
In interactive task learning (ITL), AI agents learn new capabilities from limited human instruction provided during task execution. STAND is a new method of data-efficient rule precondition induction specifically designed for these human-in-the-loop training scenarios. A key feature of STAND is its self-awareness of its own learning -- it can provide accurate metrics of training progress back to users. STAND beats popular methods like XGBoost, decision trees, random forests, and version spaces at small-data precondition induction tasks, and is highly accurate at estimating when its performance improves on holdout examples. In our evaluations, we find that STAND shows more monotonic improvement than other models with low rates of error recurrence. These features of STAND support a more consistent training experience, enabling human instructors to estimate when they are finished training and providing active-learning support by identifying trouble spots where more training is required.
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