接受量化推理

L. Kovács, Simon Robillard, A. Voronkov
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引用次数: 46

摘要

有限项代数理论为描述函数式语言的语义提供了一个自然的框架。对项代数进行有效推理的能力对于通过归纳定义的数据类型(如列表和树)对函数式或命令式程序进行自动化程序分析和验证至关重要。然而,由于有限项代数的理论不是有限公理化的,关于项代数的量化性质的推理是具有挑战性的。在本文中,我们讨论了关于操作项代数的程序的性质的完全一阶推理,并描述了用一阶定理证明的两种方法。我们的第一种方法是使用有限个数的陈述对项的理论进行保守扩展,而我们的第二种方法依赖于用附加的推理规则扩展一阶定理证明者的叠加演算。我们在一阶定理证明者Vampire中实现了我们的工作,并在大量归纳数据类型基准测试以及博弈论约束条件下对其进行了评估。我们的实验结果表明,我们的方法能够为许多以前用最先进的方法无法解决的难题找到证明。我们还表明,实现我们方法的Vampire优于能够处理归纳数据类型的现有SMT求解器。
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Coming to terms with quantified reasoning
The theory of finite term algebras provides a natural framework to describe the semantics of functional languages. The ability to efficiently reason about term algebras is essential to automate program analysis and verification for functional or imperative programs over inductively defined data types such as lists and trees. However, as the theory of finite term algebras is not finitely axiomatizable, reasoning about quantified properties over term algebras is challenging. In this paper we address full first-order reasoning about properties of programs manipulating term algebras, and describe two approaches for doing so by using first-order theorem proving. Our first method is a conservative extension of the theory of term alge- bras using a finite number of statements, while our second method relies on extending the superposition calculus of first-order theorem provers with additional inference rules. We implemented our work in the first-order theorem prover Vampire and evaluated it on a large number of inductive datatype benchmarks, as well as game theory constraints. Our experimental results show that our methods are able to find proofs for many hard problems previously unsolved by state-of-the-art methods. We also show that Vampire implementing our methods outperforms existing SMT solvers able to deal with inductive data types.
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