English(EN) Learning Lifted Action Models from Traces with Minimal Information About Actions and States

新算法从最少的状态和动作数据中学习动作模型

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-18 16:33

研究人员开发了从轨迹中学习STRIPS+动作模型的新算法，即使关于动作和状态的信息很少。这项工作通过不假设完全状态可观测性，考虑了无状态可观测性、部分谓词可观测性和完全谓词可观测性的场景，从而放宽了先前的限制。研究结果通过实验结果支持，确定了可以从轨迹中学习等效域的条件。 AI

影响推进了从有限数据中学习AI规划模型的方法，有可能提高智能体在复杂环境中的能力。

排序理由学术论文，详细介绍了新算法和实验结果。[lever_c_demoted from research: ic=1 ai=1.0]

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本文如何被排名

Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 学术论文，详细介绍了新算法和实验结果。[lever_c_demoted from research: ic=1 ai=1.0]
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arXiv cs.AI TIER_1 English(EN) · Hector Geffner · 2026-05-18 16:33

从信息极少的动作和状态轨迹中学习动作模型

It has been recently shown that lifted STRIPS models can be learned correctly and efficiently from action traces alone; i.e., applicable action sequences from a hidden STRIPS model. The result is remarkable because the states are not assumed to be observable at all, and yet it is…