论文标题
基于文本的动作模型收购计划
Text-Based Action-Model Acquisition for Planning
论文作者
论文摘要
尽管有一些方法能够从计划痕迹中学习行动模型，但与平面痕迹相比，从文本观察中学习动作模型的研究尚无研究。在本文中，我们通过整合约束满意度和自然语言处理技巧提出了一种从自然语言文本中学习动作模型的新方法。具体来说，我们首先构建了一种新颖的语言模型来从文本中提取计划跟踪，然后建立一组约束，以基于提取的计划痕迹生成动作模型。之后，我们迭代地改进语言模型和约束，直到达到收敛的语言模型和动作模型为止。我们从经验上表明，我们的方法既有效又有效。
Although there have been approaches that are capable of learning action models from plan traces, there is no work on learning action models from textual observations, which is pervasive and much easier to collect from real-world applications compared to plan traces. In this paper we propose a novel approach to learning action models from natural language texts by integrating Constraint Satisfaction and Natural Language Processing techniques. Specifically, we first build a novel language model to extract plan traces from texts, and then build a set of constraints to generate action models based on the extracted plan traces. After that, we iteratively improve the language model and constraints until we achieve the convergent language model and action models. We empirically exhibit that our approach is both effective and efficient.
