Propositional satisfiability (or satisfiability) and answer set programming are two closely related subareas of Artificial Intelligence that are used to model and solve difficult combinatorial search problems. Satisfiability solvers and answer set solvers are the software systems that find satisfying interpretations and answer sets for given propositional formulas and logic programs, respectively. These systems are closely related in their common design patterns. In satisfiability, a propositional formula is used to encode problem specifications in a way that its satisfying interpretations correspond to the solutions of the problem. To find solutions to a problem it is then sufficient to use a satisfiability solver on a corresponding formula. Niemelä, Marek, and Truszczyński coined answer set programming paradigm in 1999: in this paradigm a logic program encodes problem specifications in a way that the answer sets of a logic program represent the solutions of the problem. As a result, to find solutions to a problem it is sufficient to use an answer set solver on a corresponding program. These parallels that we just draw between paradigms naturally bring up a question: what is a fundamental difference between the two? This paper takes a close look at this question.

中文翻译：

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什么是命题可满足性的答案集编程

命题可满足性（或可满足性）和答案集编程是人工智能的两个紧密相关的子区域，用于建模和解决困难的组合搜索问题。满意度求解器和答案集求解器是分别为给定的命题公式和逻辑程序找到令人满意的解释和答案集的软件系统。这些系统的通用设计模式密切相关。在可满足性方面，命题公式以其令人满意的解释与问题的解决方案相对应的方式用于编码问题规范。为了找到问题的解决方案，在相应的公式上使用满足性求解器就足够了。Niemelä，Marek和Truszczyński于1999年提出了答案集编程范例：在该范例中，逻辑程序以一种方式来对问题规范进行编码，以使逻辑程序的答案集代表问题的解决方案。结果，要找到问题的解决方案，在相应程序上使用答案集求解器就足够了。我们只是在范式之间得出的这些相似之处自然会提出一个问题：两者之间的根本区别是什么？本文仔细研究了这个问题。