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 on minone, 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.

翻译：搜索优化问题在科学和工程领域中普遍存在。人工智能长期致力于开发面向搜索优化问题求解与建模的搜索算法及声明式编程语言。自动推理与知识表示是人工智能中尤其专注于这些发展的子领域。许多流行的自动推理范式为用户提供了支持优化语句的语言：例如回答集编程或基于minone的MaxSAT。这些范式在语言表达方式及对计算结果质量条件的描述上存在显著差异。本文提出了名为权重系统的统一框架，该框架消除了不同范式间的句法差异，揭示了各范式提供的优化语句在本质上的共性与差异。这种统一视角在自动推理与知识表示的优化及模块化研究中具有重要的简化与解释潜力，同时为研究者提供了证明不同框架形式性质、桥接框架体系以及促进翻译求解器开发的便捷工具。