E 2.0 User Manual

Schulz, Stephan

doi:10.29007/m4jw

Cited by 9 publications

(7 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Inverted lookahead in the incomplete (1012) and complete (12) version end up last in the table, which can be seen as a confirmation of our hypothesis from Sect. 5.…”

Section: Experimental Evaluationsupporting

confidence: 85%

See 1 more Smart Citation

Selecting the Selection

Reger,

Suda,

Voronkov

et al. 2016

Preprint

View full text Add to dashboard Cite

Modern saturation-based Automated Theorem Provers typically implement the superposition calculus for reasoning about first-order logic with or without equality. Practical implementations of this calculus use a variety of literal selections and term orderings to tame the growth of the search space and help steer proof search. This paper introduces the notion of lookahead selection that estimates (looks ahead) the effect of selecting a particular literal on the number of immediate children of the given clause and selects to minimize this value. There is also a case made for the use of incomplete selection strategies that attempt to restrict the search space instead of satisfying some completeness criteria. Experimental evaluation in the VAMPIRE theorem prover shows that both lookahead selection and incomplete selection significantly contribute to solving hard problems unsolvable by other methods.

show abstract

“…Inverted lookahead in the incomplete (1012) and complete (12) version end up last in the table, which can be seen as a confirmation of our hypothesis from Sect. 5.…”

Section: Experimental Evaluationsupporting

confidence: 85%

“…We consider the following five literal selection strategies adapted from E (as mentioned in the prover's manual [12]):…”

Section: E Prover Inspiredmentioning

confidence: 99%

Selecting the Selection

Reger,

Suda,

Voronkov

et al. 2016

Preprint

View full text Add to dashboard Cite

show abstract

“…In addition, it implements a large array of simplification rules, the most important of which are unconditional rewriting, subsumption, equational literal cutting and contextual literal cutting. A more detailed description of the calculus (and its realization in the proof procedure) is provided in the manual [19].…”

Section: Calculus and Implementationmentioning

confidence: 99%

Faster, Higher, Stronger: E 2.3

Schulz

Cruanes

Vukmirović

2019

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

E 2.3 is a theorem prover for many-sorted first-order logic with equality. We describe the basic logical and software architecture of the system, as well as core features of the implementation. We particularly discuss recently added features and extensions, including the extension to many-sorted logic, optional limited support for higher-order logic, and the integration of SAT techniques via PicoSAT. Minor additions include improved support for TPTP standard features, always-on internal proof objects, and lazy orphan removal. The paper also gives an overview of the performance of the system, and describes ongoing and future work.

show abstract

“…Several general-purpose precedence generating schemes are available to ATP users, such as the successful invfreq scheme in E [33], which orders the symbols by the number of occurrences in the input problem. However, experiments with random precedences indicate that the existing schemes often fail to come close to the optimum precedence [28], suggesting room for further improvements.…”

Section: Introductionmentioning

confidence: 99%

Neural Precedence Recommender

Bártek

Suda²

2021

Lecture Notes in Computer Science

View full text Add to dashboard Cite

The state-of-the-art superposition-based theorem provers for first-order logic rely on simplification orderings on terms to constrain the applicability of inference rules, which in turn shapes the ensuing search space. The popular Knuth-Bendix simplification ordering is parameterized by symbol precedence—a permutation of the predicate and function symbols of the input problem’s signature. Thus, the choice of precedence has an indirect yet often substantial impact on the amount of work required to complete a proof search successfully.This paper describes and evaluates a symbol precedence recommender, a machine learning system that estimates the best possible precedence based on observations of prover performance on a set of problems and random precedences. Using the graph convolutional neural network technology, the system does not presuppose the problems to be related or share a common signature. When coupled with the theorem prover Vampire and evaluated on the TPTP problem library, the recommender is found to outperform a state-of-the-art heuristic by more than 4 % on unseen problems.

show abstract

E 2.0 User Manual

Cited by 9 publications

References 11 publications

Selecting the Selection

Selecting the Selection

Faster, Higher, Stronger: E 2.3

Neural Precedence Recommender

Contact Info

Product

Resources

About