论文标题
统一的框架以在非树树形式主义中进行优化
Unifying Framework for Optimizations in non-boolean Formalisms
论文作者
论文摘要
在科学和工程领域中，搜索优化的问题很多。长期以来，人工智能有助于搜索算法和旨在解决搜索优化问题的搜索算法和声明性编程语言的发展。自动推理和知识表示是AI的子场，这些子场尤其归属这些发展。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。召回整数线性编程，MaxSat，优化满意度模型理论和（约束）答案集编程。这些范式在其语言上以它们在计算解决方案上表达质量条件的方式差异很大。在这里，我们提出了一个所谓的扩展权重系统的统一框架，以消除范式之间的句法区别。它们使我们可以看到不同自动推理语言提供的优化语句之间的基本相似之处和差异。我们还研究了拟议系统的形式特性，这些系统立即转化为可以在我们的框架内捕获的范式的形式属性。在逻辑编程（TPLP）的理论和实践中考虑的。
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. Recall integer linear programming, MaxSAT, optimization satisfiability modulo theory, and (constraint) answer set programming. These paradigms vary significantly in their languages in ways they express quality conditions on computed solutions. Here we propose a unifying framework of so called extended weight systems that eliminates syntactic distinctions between paradigms. They allow us to see essential similarities and differences between optimization statements provided by distinct automated reasoning languages. We also study formal properties of the proposed systems that immediately translate into formal properties of paradigms that can be captured within our framework. Under consideration in Theory and Practice of Logic Programming (TPLP).
