论文标题
科学的神经束编程
Neurosymbolic Programming for Science
论文作者
论文摘要
Neurosymbolic编程（NP）技术有可能加速科学发现。这些模型结合了神经和符号组件，以使用高级概念或已知约束来从数据中学习复杂的模式和表示形式。 NP技术可以与科学家（例如先验知识和实验环境）的符号领域知识接触，以产生可解释的产出。我们确定了当前NP模型和科学工作流程之间的机遇和挑战，其中包括科学行为分析的现实示例：使NP能够广泛地用于自然和社会科学的工作流程。
Neurosymbolic Programming (NP) techniques have the potential to accelerate scientific discovery. These models combine neural and symbolic components to learn complex patterns and representations from data, using high-level concepts or known constraints. NP techniques can interface with symbolic domain knowledge from scientists, such as prior knowledge and experimental context, to produce interpretable outputs. We identify opportunities and challenges between current NP models and scientific workflows, with real-world examples from behavior analysis in science: to enable the use of NP broadly for workflows across the natural and social sciences.
