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arXiv 2004.12891cs.LO
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概率λ-演算中不懒惰的好处

The Benefit of Being Non-Lazy in Probabilistic λ-calculus

Gianluca Curzi, Michele Pagani

摘要:

我们认为概率应用双相似性(PAB),共归纳关系比较的概率无类型的lambda条款根据一个特定的操作语义的应用行为。这个概念已经被研究了两个标准的参数传递策略,按值调用(cbv)和按名称调用(cbn),使用了一个懒惰的减少策略,而不是减少一个函数的主体。特别是,PAB已被证明是完全抽象的上下文等价的cbv,但不是在懒惰cbn。我们克服了这个问题的cbn放松懒惰的约束:我们证明,PAB是完全抽象的标准头减少上下文等价。我们的证明是基于Leventis分离定理,使用概率Nakajima树作为上下文等价类的树状表示。最后,我们还证明了不等式完全抽象失败,表明概率应用相似性严格包含在上下文前序中。

英文摘要:

We consider the probabilistic applicative bisimilarity (PAB), a coinductive relation comparing the applicative behaviour of probabilistic untyped lambda terms according to a specific operational semantics. This notion has been studied with respect to the two standard parameter passing policies, call-by-value (cbv) and call-by-name (cbn), using a lazy reduction strategy not reducing within the body of a function. In particular, PAB has been proven to be fully abstract with respect to the contextual equivalence in cbv but not in lazy cbn. We overcome this issue of cbn by relaxing the laziness constraint: we prove that PAB is fully abstract with respect to the standard head reduction contextual equivalence. Our proof is based on the Leventis Separation Theorem, using probabilistic Nakajima trees as a tree-like representation of the contextual equivalence classes. Finally, we prove also that the inequality full abstraction fails, showing that the probabilistic applicative similarity is strictly contained in the contextual preorder.

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