Answer set programming (ASP) is a popular nonmonotonic-logic based paradigm for knowledge representation and solving combinatorial problems. Computing the answer set of an ASP program is NP-hard in general, and researchers have been investing significant effort to speed it up. The majority of current ASP solvers employ SAT solver-like technology to find these answer sets. As a result, justification for why a literal is in the answer set is hard to produce. There are dependency graph based approaches to find answer sets, but due to the representational limitations of dependency graphs, such approaches are limited. This paper proposes a novel dependency graph-based approach for finding answer sets in which conjunction of goals is explicitly represented as a node which allows arbitrary answer set programs to be uniformly represented. Our representation preserves causal relationships allowing for justification for each literal in the answer set to be elegantly found. In this paper, we explore two different approaches based on the graph representation: bottom-up and top-down. The bottom-up approach finds models by assigning truth values along with the topological order, while the top-down approach generates models starting from the constraints.

中文翻译：

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基于图的答案集编程求解器系统

答案集编程 (ASP) 是一种流行的基于非单调逻辑的范式，用于知识表示和解决组合问题。计算 ASP 程序的答案集通常是 NP 难的，研究人员一直在投入大量精力来加速它。当前的大多数 ASP 求解器都采用类似 SAT 求解器的技术来查找这些答案集。因此，很难解释为什么文字在答案集中。有基于依赖图的方法来寻找答案集，但由于依赖图的表示限制，这些方法是有限的。本文提出了一种新的基于依赖图的方法来寻找答案集，其中目标的联合被明确表示为一个节点，该节点允许统一表示任意答案集程序。我们的表示保留了因果关系，允许优雅地找到答案集中每个文字的理由。在本文中，我们探索了两种基于图表示的不同方法：自底向上和自顶向下。自下而上的方法通过分配真值和拓扑顺序来找到模型，而自上而下的方法从约束开始生成模型。