Complex reasoning problems are most clearly and easily specified using logical rules, but require recursive rules with aggregation such as counts and sums for practical applications. Unfortunately, the meaning of such rules has been a significant challenge, leading to many disagreeing semantics. This paper describes a unified semantics for recursive rules with aggregation, extending the unified founded semantics and constraint semantics for recursive rules with negation. The key idea is to support simple expression of the different assumptions underlying different semantics, and orthogonally interpret aggregation operations using their simple usual meaning. We present formal definition of the semantics, prove important properties of the semantics, and compare with prior semantics. Besides exact inference over aggregations, we present an efficient approximation that gives precise answers to all examples we have studied from the literature. We also apply our semantics to a wide range of challenging examples, and show that our semantics is simple and matches the desired results in all cases. Finally, we describe experiments on the most challenging examples, exhibiting unexpectedly superior performance over well-known systems when they can compute correct answers.
翻译:复杂的推理问题最清楚、最容易地用逻辑规则来说明,但需要重新制定规则,总合起来,例如用于实际应用的计数和计数。不幸的是,这些规则的含义是一个重大挑战,导致许多不一致的语义学。本文描述了一个统一的语义,用于循环规则,集成,扩展统一建立的语义学和制约语义学,用于循环规则,否定。关键的想法是支持简单表达不同语义学背后的不同假设,以及使用其简单的通常含义对集合作业进行正统解释。我们提出了语义学的正式定义,证明语义学的重要特性,并与先前的语义学作了比较。除了精确的推论外,我们还提出了一个有效的近似法,为我们从文献中研究的所有例子提供了准确的答案。我们还将语义学应用于一系列具有挑战性的例子,并表明我们的语义学简单,在所有案例中都符合理想的结果。最后,我们描述了最具挑战性的例子,在能够解析答案时,我们展示出出出出人意料的优于众所周知的系统。