Interactive synthesis of temporal specifications from examples and natural language
Interactive synthesis of temporal specifications from examples and natural language
复制标题
来自示例和自然语言的时间规范的交互式合成
DOI：
10.1145/3428269
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发表时间：
2020
影响因子：
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通讯作者：
R. Majumdar
中科院分区：
文献类型：
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作者：
I. Gavran;Eva Darulova;R. Majumdar
出于机器人技术中的应用，我们考虑的任务，合成线性时序逻辑（LTL）规范的基础上的例子和自然语言描述。虽然LTL是一种灵活，表达力强，明确的语言来描述机器人任务，但对于非专家用户来说，它往往具有挑战性。在本文中，我们提出了一个交互式的方法合成LTL规格从一个单一的例子跟踪和自然语言描述。交互仅限于向用户显示少量的行为示例，由用户决定它们是否表现出原始意图。我们的方法生成候选LTL规范和区分使用编码到优化模理论问题的例子。此外，我们使用语法扩展机制和语义解析器来概括合成的规格参数的任务描述供后续使用。我们在工具LtlTalk中的实现始于一种特定于领域的语言，该语言映射到LTL的一个片段，并通过基于示例的用户交互对其进行扩展，从而实现类似自然语言的机器人编程，同时保持形式语言的表达能力和精度。我们的实验表明，合成方法是精确的，快速的，只问了几个问题的用户，我们演示了在一个案例研究中LtlTalk如何概括从合成的任务，但看不见的，任务。
Motivated by applications in robotics, we consider the task of synthesizing linear temporal logic (LTL) specifications based on examples and natural language descriptions. While LTL is a flexible, expressive, and unambiguous language to describe robotic tasks, it is often challenging for non-expert users. In this paper, we present an interactive method for synthesizing LTL specifications from a single example trace and a natural language description. The interaction is limited to showing a small number of behavioral examples to the user who decides whether or not they exhibit the original intent. Our approach generates candidate LTL specifications and distinguishing examples using an encoding into optimization modulo theories problems. Additionally, we use a grammar extension mechanism and a semantic parser to generalize synthesized specifications to parametric task descriptions for subsequent use. Our implementation in the tool LtlTalk starts with a domain-specific language that maps to a fragment of LTL and expands it through example-based user interactions, thus enabling natural language-like robot programming, while maintaining the expressive power and precision of a formal language. Our experiments show that the synthesis method is precise, quick, and asks only a few questions to the users, and we demonstrate in a case study how LtlTalk generalizes from the synthesized tasks to other, yet unseen, tasks.
DOI：
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发表时间：
2018
期刊：
Thirty-third Conference on Neural Information Processing Systems (NeurIPS
影响因子：
--
作者：
VazquezChanlatte, Marcell;Jha, Susmit;Tiwari, Ashish;Seshia, Sanjit
通讯作者：
Seshia, Sanjit