论文标题
AMLSI：一种新颖的精确动作模型学习算法
AMLSI: A Novel Accurate Action Model Learning Algorithm
论文作者
论文摘要
本文介绍了基于语法诱导的新方法，称为AMLSI动作模型与状态机相互作用。 AMLSI方法不需要计划轨迹的培训数据集即可工作。 AMLSI通过反复试验进行：它查询系统以随机生成的动作序列学习，并观察系统的状态过渡，然后AMLSI返回一个与系统相对应的PDDL域。领域学习的一个关键问题是能够使用学识渊博的域进行计划。通常，小小的学习错误会导致一个无法使用的领域。与其他算法不同，我们表明AMLSI能够通过从部分和嘈杂的观察结果中学习锁定锁定，并具有足够的精度，以允许计划者解决新问题。
This paper presents new approach based on grammar induction called AMLSI Action Model Learning with State machine Interactions. The AMLSI approach does not require a training dataset of plan traces to work. AMLSI proceeds by trial and error: it queries the system to learn with randomly generated action sequences, and it observes the state transitions of the system, then AMLSI returns a PDDL domain corresponding to the system. A key issue for domain learning is the ability to plan with the learned domains. It often happens that a small learning error leads to a domain that is unusable for planning. Unlike other algorithms, we show that AMLSI is able to lift this lock by learning domains from partial and noisy observations with sufficient accuracy to allow planners to solve new problems.
