HOL(y)Hammer: Online ATP Service for HOL Light

Kaliszyk, Cezary; Urban, Josef

doi:10.1007/s11786-014-0182-0

Cited by 73 publications

(65 citation statements)

References 27 publications

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“…HOL y Hammer, for HOL Light, provides such a facility [50]. The "four zeros" mentioned in the introduction were chosen with casual Isabelle users in mind, but some teams are ready to spend more time and effort setting up their environment if they know it will bring significant gains.…”

Section: Case Study: Microkernel Verificationmentioning

confidence: 99%

A Learning-Based Fact Selector for Isabelle/HOL

et al. 2016

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Sledgehammer integrates automatic theorem provers in the proof assistant Isabelle/HOL. A key component, the fact selector, heuristically ranks the thousands of facts (lemmas, definitions, or axioms) available and selects a subset, based on syntactic similarity to the current proof goal. We introduce MaSh, an alternative that learns from successful proofs. New challenges arose from our "zero click" vision: MaSh integrates seamlessly with the users' workflow, so that they benefit from machine learning without having to install software, set up servers, or guide the learning. MaSh outperforms the old fact selector on large formalizations.

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Section: Case Study: Microkernel Verificationmentioning

confidence: 99%

A Learning-Based Fact Selector for Isabelle/HOL

et al. 2016

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“…Since we are very interested in seeing the probabilistic parsing in action, we deploy the whole parsing toolchain as an online service 8 that further uses the HOL(y)Hammer AI/ATP system [5] for even stronger semantic filtering. The service allows HOL Light and Flyspeck users to write ambiguous formulas using many common ambiguous symbols and omitting brackets and casting functors.…”

Section: Probabilistic Parsing and Its Extensionsmentioning

confidence: 99%

Learning to Parse on Aligned Corpora (Rough Diamond)

Kaliszyk

Urban

Vyskočil

2015

Interactive Theorem Proving

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Abstract. One of the first big hurdles that mathematicians encounter when considering writing formal proofs is the necessity to get acquainted with the formal terminology and the parsing mechanisms used in the large ITP libraries. This includes the large number of formal symbols, the grammar of the formal languages and the advanced mechanisms instrumenting the proof assistants to correctly understand the formal expressions in the presence of ubiquitous overloading. In this work we start to address this problem by developing approximate probabilistic parsing techniques that autonomously train disambiguation on large corpora. Unlike in standard natural language processing, we can filter the resulting parse trees by strong ITP and AR semantic methods such as typechecking and automated theorem proving, and even let the probabilistic methods self-improve based on such semantic feedback. We describe the general motivation and our first experiments, and build an online system for parsing ambiguous formulas over the Flyspeck library.

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“…This potential is already being realized in the field of automated reasoning, where proof exchange for theorem proving [11,40] is routinely used to enable verified cooperation among a vast variety of proof-producing automated deduction tools [66], as exemplified in systems such as Sledgehammer [50] and HOL(y)Hammer [41].…”

Section: Introductionmentioning

confidence: 99%

Greedy pebbling for proof space compression

Fellner

Paleo

2017

Int J Softw Tools Technol Transfer

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Automated reasoning tools for the verification and synthesis of software often produce proofs to allow independent certification of the correctness of the produced solutions. As proofs can be large, this paper considers the problem of compressing proofs with respect to their space, which is approximately proportional to the memory necessary to check them. Proof checking with a small amount of available memory is analogous to playing a pebbling game with a small number of pebbles. This paper exploits this analogy and describes novel algorithms for playing a pebbling game. The sequence of moves executed in the pebbling game then corresponds to an improved topological ordering of the nodes of the proof, leading to smaller memory consumption when the proof is checked. Because the number of possible pebbling strategies and topological orderings is too large, brute-force approaches to find optimal solutions are impractical, and hence, the new pebbling algorithms proposed here are based on heuristics for finding good, though not necessarily optimal, solutions. The algorithms are evaluated on the task of compressing the space of thousands of propositional resolution proofs generated by SAT-and SMT-solvers.

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HOL(y)Hammer: Online ATP Service for HOL Light

Cited by 73 publications

References 27 publications

A Learning-Based Fact Selector for Isabelle/HOL

A Learning-Based Fact Selector for Isabelle/HOL

Learning to Parse on Aligned Corpora (Rough Diamond)

Greedy pebbling for proof space compression

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