Integrating symbolic constraints into deep learning models could make them more robust, interpretable, and data-efficient. Still, it remains a time-consuming and challenging task. Existing frameworks like DomiKnowS help this integration by providing a high-level declarative programming interface, but they still assume the user is proficient with the library's specific syntax. We propose AgenticDomiKnowS (ADS) to eliminate this dependency. ADS translates free-form task descriptions into a complete DomiKnowS program using an agentic workflow that creates and tests each DomiKnowS component separately. The workflow supports optional human-in-the-loop intervention, enabling users familiar with DomiKnowS to refine intermediate outputs. We show how ADS enables experienced DomiKnowS users and non-users to rapidly construct neuro-symbolic programs, reducing development time from hours to 10-15 minutes.

翻译：将符号约束集成到深度学习模型中，可以增强模型的鲁棒性、可解释性和数据效率。然而，这仍然是一项耗时且具有挑战性的任务。现有的框架（如 DomiKnowS）通过提供高级声明式编程接口来促进这种集成，但它们仍然假设用户精通该库的特定语法。我们提出了 AgenticDomiKnowS（ADS）来消除这种依赖性。ADS 利用一种智能体工作流，将自由形式的任务描述翻译成一个完整的 DomiKnowS 程序，该工作流会分别创建和测试每个 DomiKnowS 组件。该工作流支持可选的人工介入干预，使熟悉 DomiKnowS 的用户能够优化中间输出。我们展示了 ADS 如何使有经验的 DomiKnowS 用户和非用户都能快速构建神经符号程序，将开发时间从数小时减少到 10-15 分钟。