By Dominique Bolignano (auth.), Sandrine Blazy, Christine Paulin-Mohring, David Pichardie (eds.)
This e-book constitutes the refereed court cases of the 4th foreign convention on Interactive Theorem Proving, ITP 2013, held in Rennes, France, in July 2013. The 26 general complete papers awarded including 7 tough diamond papers, three invited talks, and a couple of invited tutorials have been conscientiously reviewed and chosen from sixty six submissions. The papers are geared up in topical sections reminiscent of application verfication, defense, formalization of arithmetic and theorem prover development.
Read Online or Download Interactive Theorem Proving: 4th International Conference, ITP 2013, Rennes, France, July 22-26, 2013. Proceedings PDF
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Extra info for Interactive Theorem Proving: 4th International Conference, ITP 2013, Rennes, France, July 22-26, 2013. Proceedings
Sample text
The proof is then reconstructed in Isabelle by a suitable proof text, typically a call to the built-in resolution prover Metis [16]. Example 1. Given the goal map f xs = ys =⇒ zip (rev xs) (rev ys) = rev (zip xs ys) MePo selects 1000 facts: rev_map, rev_rev_ident, . . , add_numeral_special(3). The prover E, among others, quickly finds a minimal proof involving the 4th and 16th facts: zip_rev: length xs = length ys =⇒ zip (rev xs) (rev ys) = rev (zip xs ys) length_map: length (map f xs) = length xs Example 2.
1 We consistently use modules as name spaces, so that the short names like class get a qualifying prefix EQ. once the name space definition is finished. class. ✞ Module EQ. Record class (T : Type) := Class { cmp : T → T → Prop }. Structure type := Pack { obj : Type; class_of : class obj }. Definition op (e : type) : obj e → obj e → Prop := let ’Pack _ (Class the_cmp) := e in the_cmp. Check op. obj e → Prop *) Arguments op {e} x y : simpl never. Arguments Class {T} cmp. type record. op projection, whose type is displayed in the above code.
We introduce MaSh, an alternative that learns from successful proofs. New challenges arose from our “zero-click” vision: MaSh should integrate seamlessly with the users’ workflow, so that they benefit from machine learning without having to install software, set up servers, or guide the learning. The underlying machinery draws on recent research in the context of Mizar and HOL Light, with a number of enhancements. MaSh outperforms the old relevance filter on large formalizations, and a particularly strong filter is obtained by combining the two filters.