An abstract view on optimizations in propositional frameworks

IF 1.2 4区计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Annals of Mathematics and Artificial Intelligence Pub Date : 2023-12-16 DOI:10.1007/s10472-023-09914-6

Yuliya Lierler

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引用次数: 0

Abstract

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.

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关于命题框架优化的抽象观点

搜索/优化问题在科学和工程领域比比皆是。长期以来，人工智能一直致力于开发搜索算法和声明式编程语言，以解决搜索/优化问题并为其建模。自动推理和知识表示是人工智能的两个子领域，它们在这些领域的发展尤为突出。许多流行的自动推理范式都为用户提供了支持优化语句的语言：如答案集编程、MaxSAT 或 min-one 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权重系统的统一框架，它消除了不同范式之间的语法区别，使我们能够看到不同范式所提供的优化语句之间的本质异同。在自动推理和知识表示中的优化和模块化研究中，这种统一的观点具有重要的简化和解释潜力。它还为研究人员提供了一个方便的工具，用于证明不同框架的形式属性、连接这些框架以及促进转化求解器的开发。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Annals of Mathematics and Artificial Intelligence 工程技术-计算机：人工智能

CiteScore

3.00

自引率

8.30%

发文量

审稿时长

>12 weeks

期刊介绍： Annals of Mathematics and Artificial Intelligence presents a range of topics of concern to scholars applying quantitative, combinatorial, logical, algebraic and algorithmic methods to diverse areas of Artificial Intelligence, from decision support, automated deduction, and reasoning, to knowledge-based systems, machine learning, computer vision, robotics and planning. The journal features collections of papers appearing either in volumes (400 pages) or in separate issues (100-300 pages), which focus on one topic and have one or more guest editors. Annals of Mathematics and Artificial Intelligence hopes to influence the spawning of new areas of applied mathematics and strengthen the scientific underpinnings of Artificial Intelligence.