Extracting Planning Operators from Instructional Texts for Behaviour Interpretation
Extracting Planning Operators from Instructional Texts for Behaviour Interpretation
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DOI：
10.1007/978-3-030-00111-7_19
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发表时间：
2018-09
期刊：
影响因子：
6.1
通讯作者：
Kristina Yordanova
中科院分区：
文献类型：
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作者：
Kristina Yordanova
最近通过语言基础来理解行为的尝试表明，从教学文本自动生成规划模型是可能的。这些方法的一个缺点是，它们要么不利用文本中确定的模型元素背后的语义结构，要么手动合并具有它们之间的语义关系的概念集合。然而，要使用这样的模型来理解行为，系统还应该了解计划操作符背后的语义结构和上下文。为了解决这个问题，我们提出了一种从文本指令自动生成计划操作符的方法。该方法能够识别模型元素之间的各种层次关系、空间关系、方向关系和因果关系。这允许将上下文知识合并到正在执行的操作之外。我们从识别元素的正确性、模型搜索的复杂性、模型覆盖率和与手工制作的模型的相似性等方面对该方法进行了评估。结果表明，该方法能够生成能够解释实际任务执行的模型，并且模型可以与手工创建的模型相媲美。
Recent attempts at behaviour understanding through language grounding have shown that it is possible to automatically generate planning models from instructional texts. One drawback of these approaches is that they either do not make use of the semantic structure behind the model elements identified in the text, or they manually incorporate a collection of concepts with semantic relationships between them. To use such models for behaviour understanding, however, the system should also have knowledge of the semantic structure and context behind the planning operators. To address this problem, we propose an approach that automatically generates planning operators from textual instructions. The approach is able to identify various hierarchical, spatial, directional, and causal relations between the model elements. This allows incorporating context knowledge beyond the actions being executed. We evaluated the approach in terms of correctness of the identified elements, model search complexity, model coverage, and similarity to handcrafted models. The results showed that the approach is able to generate models that explain actual tasks executions and the models are comparable to handcrafted models.