Search/optimization problems are plentiful in scientific and engineering domains. Artificial intelligence has long contributed to the development of search algorithms and declarative programming languages geared toward solving and modeling search/optimization problems. Automated reasoning and knowledge representation are the subfields of AI that are particularly vested in these developments. Many popular automated reasoning paradigms provide users with languages supporting optimization statements: answer set programming or MaxSAT or min-one, to name a few. These paradigms vary significantly in their languages and in the ways they express quality conditions on computed solutions. Here we propose a unifying framework of so-called weight systems that eliminates syntactic distinctions between paradigms and allows us to see essential similarities and differences between optimization statements provided by paradigms. This unifying outlook has significant simplifying and explanatory potential in the studies of optimization and modularity in automated reasoning and knowledge representation. It also supplies researchers with a convenient tool for proving the formal properties of distinct frameworks; bridging these frameworks; and facilitating the development of translational solvers.

科学和工程领域中存在大量搜索/优化问题。人工智能长期以来一直致力于搜索算法和声明性编程语言的开发，旨在解决和建模搜索/优化问题。自动推理和知识表示是人工智能的子领域，特别适合这些发展。许多流行的自动推理范例为用户提供支持优化语句的语言：答案集编程或 MaxSAT 或 min-one 等等。这些范式在语言和表达计算解决方案质量条件的方式上存在很大差异。在这里，我们提出了一个所谓的权重系统的统一框架，它消除了范式之间的句法区别，并使我们能够看到范式提供的优化语句之间的本质相似点和差异。这种统一的观点在自动推理和知识表示的优化和模块化研究中具有显着的简化和解释潜力。它还为研究人员提供了一个方便的工具来证明不同框架的形式属性；连接这些框架；并促进平移求解器的开发。