论文标题
噪声数据的归纳计划综合
Inductive Program Synthesis Over Noisy Data
论文作者
论文摘要
我们提出了一个新的框架和相关的综合算法，用于噪声数据，即可能包含不正确/损坏的输入输出示例的数据。该框架基于有限树自动机的扩展名，称为{\ em加权有限树自动机}。我们展示了如何应用此框架来制定和解决噪声数据的各种程序合成问题。我们实施的系统在Sygus 2018 Benchmark Suite中运行的已实施系统的结果强调了它在面对嘈杂的数据集中成功合成程序的能力，包括即使数据集中的每个输入输出示例都可以构成正确的程序，即使数据集中的每个输入输出示例都损坏了。
We present a new framework and associated synthesis algorithms for program synthesis over noisy data, i.e., data that may contain incorrect/corrupted input-output examples. This framework is based on an extension of finite tree automata called {\em weighted finite tree automata}. We show how to apply this framework to formulate and solve a variety of program synthesis problems over noisy data. Results from our implemented system running on problems from the SyGuS 2018 benchmark suite highlight its ability to successfully synthesize programs in the face of noisy data sets, including the ability to synthesize a correct program even when every input-output example in the data set is corrupted.
