To solve hard problems, AI relies on a variety of disciplines such as logic, probabilistic reasoning, machine learning and mathematical programming. Although it is widely accepted that solving real-world problems requires an integration amongst these, contemporary representation methodologies offer little support for this. In an attempt to alleviate this situation, we introduce a new declarative programming framework that provides abstractions of well-known problems such as SAT, Bayesian inference, generative models, and convex optimization. The semantics of programs is defined in terms of first-order structures with semiring labels, which allows us to freely combine and integrate problems from different AI disciplines.

中文翻译：

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半环规划：广义和积问题的声明性框架

为了解决难题，人工智能依赖于各种学科，如逻辑、概率推理、机器学习和数学编程。尽管人们普遍认为解决现实世界的问题需要在这些问题之间进行整合，但当代表示方法对此几乎没有提供支持。为了缓解这种情况，我们引入了一种新的声明式编程框架，该框架提供了众所周知的问题的抽象，例如 SAT、贝叶斯推理、生成模型和凸优化。程序的语义是根据带有半环标签的一阶结构定义的，这使我们可以自由地组合和集成来自不同 AI 学科的问题。