E-MaLeS 1.1

Kühlwein, Daniel; Schulz, Stephan; Urban, Josef

doi:10.1007/978-3-642-38574-2_28

Cited by 13 publications

(10 citation statements)

References 10 publications

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“…E-MaLeS 1.1 [8] changed the learning from classification to regression. Like E-MaLeS 1.0, E-MaLeS 1.1 learned from (an updated version of) Schulz's data.…”

Section: Casc@turing and Casc-j6mentioning

confidence: 99%

“…This paper introduces MaLeS (Machine Learning (of) Strategies), a learning-based framework for automatic tuning and configuration of ATPs. It is based on and supersedes E-MaLeS 1.0 [10] and E-MaLeS 1.1 [8]. The goal of MaLeS is to help ATP users fine-tune an ATP to their problems, and give developers a simple tool for finding good search strategies and creating strategy schedules.…”

Section: Introductionmentioning

confidence: 99%

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MaLeS: A Framework for Automatic Tuning of Automated Theorem Provers

Kühlwein

Urban

2015

J Autom Reasoning

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MaLeS is an automatic tuning framework for automated theorem provers. It provides solutions for both the strategy finding as well as the strategy scheduling problem. This paper describes the tool and the methods used in it, and evaluates its performance on three automated theorem provers: E, LEO-II and Satallax. On a representative subset of the TPTP library a MaLeS-tuned prover solves on average 8.67 % more problems than the prover with its default settings.

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“…E-MaLeS 1.1 [8] changed the learning from classification to regression. Like E-MaLeS 1.0, E-MaLeS 1.1 learned from (an updated version of) Schulz's data.…”

Section: Casc@turing and Casc-j6mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MaLeS: A Framework for Automatic Tuning of Automated Theorem Provers

Kühlwein

Urban

2015

J Autom Reasoning

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“…Over the past decade, many improvements and extensions of E have been developed from different perspectives, where some successful ones are E.T. [27] and E-Males [28,29]. Their improvements are mainly from the perspectives of the effective selection of axioms and the effective use of strategies for the improvement of E. It is an open question whether E can be improved from the inference mechanism point of view and the S-CS rule can be applied effectively to E. The present work introduces a novel ATP system for first-order logic, which goes one-step further to answer these questions.…”

Section: Introductionmentioning

confidence: 99%

CSE_E 1.0: An Integrated Automated Theorem Prover for First-Order Logic

Cao

et al. 2019

Symmetry

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First-order logic is an important part of mathematical logic, and automated theorem proving is an interdisciplinary field of mathematics and computer science. The paper presents an automated theorem prover for first-order logic, called C S E _ E 1.0, which is a combination of two provers contradiction separation extension (CSE) and E, where CSE is based on the recently-introduced multi-clause standard contradiction separation (S-CS) calculus for first-order logic and E is the well-known equational theorem prover for first-order logic based on superposition and rewriting. The motivation of the combined prover C S E _ E 1.0 is to (1) evaluate the capability, applicability and generality of C S E _ E , and (2) take advantage of novel multi-clause S-CS dynamic deduction of CSE and mature equality handling of E to solve more and harder problems. In contrast to other improvements of E, C S E _ E 1.0 optimizes E mainly from the inference mechanism aspect. The focus of the present work is given to the description of C S E _ E including its S-CS rule, heuristic strategies, and the S-CS dynamic deduction algorithm for implementation. In terms of combination, in order not to lose the capability of E and use C S E _ E to solve some hard problems which are unsolved by E, C S E _ E 1.0 schedules the running of the two provers in time. It runs plain E first, and if E does not find a proof, it runs plain CSE, then if it does not find a proof, some clauses inferred in the CSE run as lemmas are added to the original clause set and the combined clause set handed back to E for further proof search. C S E _ E 1.0 is evaluated through benchmarks, e.g., CASC-26 (2017) and CASC-J9 (2018) competition problems (FOFdivision). Experimental results show that C S E _ E 1.0 indeed enhances the performance of E to a certain extent.

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“…Several symbolic AI/ATP methods for reasoning in the context of a large number of related theorems and proofs have been suggested and tried already, including: (i) methods (often external to the core ATP algorithms) that select relevant premises (facts) from the thousands of theorems available in such corpora ( Meng and Paulson, 2009; Hoder and Voronkov, 2011; Kühlwein et al, 2012 ), (ii) methods for internal guidance of ATP systems when reasoning in the large-theory setting ( Urban et al, 2011 ), (iii) methods that automatically evolve more and more efficient ATP strategies for the clusters of related problems from such corpora ( Urban, 2014 ), and (iv) methods that learn which of such specialized strategies to use for a new problem ( Kühlwein et al, 2013b ).…”

Section: Introduction: Automated Reasoning Over Large Mathematical LImentioning

confidence: 99%

Learning-assisted theorem proving with millions of lemmas

Kaliszyk

Urban

2015

Journal of Symbolic Computation

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Large formal mathematical libraries consist of millions of atomic inference steps that give rise to a corresponding number of proved statements (lemmas). Analogously to the informal mathematical practice, only a tiny fraction of such statements is named and re-used in later proofs by formal mathematicians. In this work, we suggest and implement criteria defining the estimated usefulness of the HOL Light lemmas for proving further theorems. We use these criteria to mine the large inference graph of the lemmas in the HOL Light and Flyspeck libraries, adding up to millions of the best lemmas to the pool of statements that can be re-used in later proofs. We show that in combination with learning-based relevance filtering, such methods significantly strengthen automated theorem proving of new conjectures over large formal mathematical libraries such as Flyspeck.

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E-MaLeS 1.1

Cited by 13 publications

References 10 publications

MaLeS: A Framework for Automatic Tuning of Automated Theorem Provers

MaLeS: A Framework for Automatic Tuning of Automated Theorem Provers

CSE_E 1.0: An Integrated Automated Theorem Prover for First-Order Logic

Learning-assisted theorem proving with millions of lemmas

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