Automatic Generation of Situation Models for Plan Recognition Problems
Automatic Generation of Situation Models for Plan Recognition Problems
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DOI：
10.26615/978-954-452-049-6_105
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发表时间：
2017-11
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影响因子：
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通讯作者：
Kristina Yordanova
中科院分区：
文献类型：
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作者：
Kristina Yordanova
最近通过语言基础进行行为理解的尝试表明，可以根据文本指令自动生成用于规划问题的模型。这些方法的一个缺点是它们要么不利用文本中标识的模型元素背后的语义结构，要么手动合并一组概念及其之间的语义关系。我们将这种知识集合称为情境模型。情境模型向模型引入了额外的上下文信息。与不使用情境模型的模型相比，它还可能降低规划问题的复杂性。为了解决这个问题，我们提出了一种从文本指令自动生成情境模型的方法。该方法能够识别各种层次、空间、方向和因果关系。我们使用情境模型自动生成 PDDL 表示法中的规划问题，并且表明，与不使用情境模型的规划模型相比，情境模型在运算符数量和分支因子方面降低了 PDDL 模型的复杂性。
Recent attempts at behaviour understanding through language grounding have shown that it is possible to automatically generate models for planning problems from textual instructions. 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. We call this collection of knowledge situation model. The situation model introduces additional context information to the model. It could also potentially reduce the complexity of the planning problem compared to models that do not use situation models. To address this problem, we propose an approach that automatically generates the situation model from textual instructions. The approach is able to identify various hierarchical, spatial, directional, and causal relations. We use the situation model to automatically generate planning problems in a PDDL notation and we show that the situation model reduces the complexity of the PDDL model in terms of number of operators and branching factor compared to planning models that do not make use of situation models.