论文标题
计划：从神经推断的规范中学习的强大计划学习
PLANS: Robust Program Learning from Neurally Inferred Specifications
论文作者
论文摘要
近年来，基于神经模型的统计程序学习的兴起是替代传统规则的系统以替代编程的替代方法。基于规则的方法可提供正确的保证，因为它们固有地捕获逻辑规则，而神经模型在原始，高维输入上更现实地扩展，并为噪声I/O规格提供阻力。我们介绍了计划（从神经推断的规范中学习），这是一种从视觉观察中获得的程序合成的混合模型，该模型依靠（i）一种神经体系结构，该神经体系结构培训，可从每个原始的单独输入（II）提取基于规则的系统的摘要，高级信息的神经体系结构，该信息使用i/o的i/o规格作为I/O的类别来捕获ARNTHSENTHESSECTIONS a ABSTERITY a PLOCTIONITY a PLOCTIONITY a BENSSITIONS a BENSSITIONS捕获。为了解决对网络输出中抗噪声的计划的关键挑战，我们基于选择性分类技术引入了I/O规格的过滤启发式启发式。我们从Karel和Vizdoom环境中的各种演示视频中获得了计划合成的最先进的性能，同时不需要培训的基本真相计划。我们在github.com/rdang-nhu/plans上提供实施。
Recent years have seen the rise of statistical program learning based on neural models as an alternative to traditional rule-based systems for programming by example. Rule-based approaches offer correctness guarantees in an unsupervised way as they inherently capture logical rules, while neural models are more realistically scalable to raw, high-dimensional input, and provide resistance to noisy I/O specifications. We introduce PLANS (Program LeArning from Neurally inferred Specifications), a hybrid model for program synthesis from visual observations that gets the best of both worlds, relying on (i) a neural architecture trained to extract abstract, high-level information from each raw individual input (ii) a rule-based system using the extracted information as I/O specifications to synthesize a program capturing the different observations. In order to address the key challenge of making PLANS resistant to noise in the network's output, we introduce a filtering heuristic for I/O specifications based on selective classification techniques. We obtain state-of-the-art performance at program synthesis from diverse demonstration videos in the Karel and ViZDoom environments, while requiring no ground-truth program for training. We make our implementation available at github.com/rdang-nhu/PLANS.
