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 towards 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: MaxSAT or answer set programming, 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 a significant simplifying and explanatory potential in the studies of optimization and modularity in automated reasoning and knowledge representation providing technical means for bridging distinct formalisms and developing translational solvers.

翻译：搜索优化问题在科学与工程领域广泛存在。人工智能长期以来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作为人工智能的子领域，尤其关注这类技术的发展。许多流行的自动推理范式为用户提供支持优化语句的语言，例如MaxSAT或回答集编程等。这些范式在语言表达方式及对计算结果质量条件的定义上存在显著差异。本文提出一个名为"权重系统"的统一框架，该框架消除了不同范式间的语法差异，使我们能够洞察各范式提供的优化语句之间的本质共性与差异。这一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显著的简化与解释潜力，为桥接不同形式化体系、开发翻译求解器提供了技术手段。