AMLSI：一种新的精确动作模型学习算法
AMLSI: A Novel Accurate Action Model Learning Algorithm
摘要：
本文提出了一种基于语法归纳的新方法，称为AMLSI动作模型学习与状态机交互。AMLSI方法不需要计划轨迹的训练数据集即可工作。AMLSI通过试错法进行：它用随机生成的动作序列询问系统进行学习，并观察系统的状态转换，然后AMLSI返回与系统对应的PDDL域。领域学习的一个关键问题是用学习的领域进行计划的能力。经常发生的情况是，一个小的学习错误会导致一个无法用于规划的域。与其他算法不同，我们表明AMLSI能够通过从部分和噪音观测中学习域来解除这一锁定，具有足够的准确性，使规划者能够解决新问题。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
