lazyCoP: Lazy Paramodulation Meets Neurally Guided Search

Rawson, Michael; Reger, Giles

doi:10.1007/978-3-030-86059-2_11

Cited by 8 publications

(6 citation statements)

References 25 publications

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“…The same is true for non saturation-based RL-friendly provers too (e.g. lazyCoP, Rawson & Reger (2021)). This monolithic approach hinders free experimentation with novel machine learning (ML) models and RL algorithms and creates unnecessary complications for ML and RL experts willing to contribute to the field.…”

Section: Statement Of Needmentioning

confidence: 78%

“…Guiding provers with RL is a hot topic. Recent projects in this domain include TRAIL (Trial Reasoner for AI that Learns) [2], FLoP (Finding Longer Proofs) [37], and lazyCoP [26]. We will now compare the new gym-saturation features with these three projects.…”

Section: Related Workmentioning

confidence: 99%

“…TRAIL claims to be reasoner-agnostic, but to our best knowledge, no one has tried it with anything but a patched E version it uses by default. [26] mentions an anonymous reviewer's suggestion to create a standalone tool for other existing systems, but we are not aware of further development in this di-rection. Quite the contrary, we have tested gym-saturation compatibility with two different provers (Vampire and iProver).…”

Section: Related Workmentioning

confidence: 99%

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gym-saturation: an OpenAI Gym environment for saturation provers

Shminke¹

2022

JOSS

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gym-saturation is an OpenAI Gym (Brockman et al., 2016) environment for reinforcement learning (RL) agents capable of proving theorems. Currently, only theorems written in a formal language of the Thousands of Problems for Theorem Provers (TPTP) library (Sutcliffe, 2017) in clausal normal form (CNF) are supported. gym-saturation implements the 'given clause' algorithm (similar to the one used in Vampire (Kovács & Voronkov, 2013) and E Prover (Schulz et al., 2019)). Being written in Python, gym-saturation was inspired by PyRes (Schulz & Pease, 2020). In contrast to the monolithic architecture of a typical Automated Theorem Prover (ATP), gym-saturation gives different agents opportunities to select clauses themselves and train from their experience. Combined with a particular agent, gym-saturation can work as an ATP. Even with a non trained agent based on heuristics, gym-saturation can find refutations for 688 (of 8257) CNF problems from TPTP v7.5.0.

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Section: Statement Of Needmentioning

confidence: 78%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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gym-saturation: an OpenAI Gym environment for saturation provers

Shminke¹

2022

JOSS

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“…Row TABX shows for comparison the number of problems that was solved by at least one of the following provers: CMProver [39,40] in at least one of several considered configurations, SETHEO 3.3 [24], S-SETHEO [14], lazyCoP 0.1 [30] and SATCoP 0.1 [31]. 14 The next two rows indicate the numbers of problems that can be solved either by SGCD in at least one of the two goal-driven configurations or by at least one of the five contributors to TABX, but not both.…”

Section: Corpus Tptpcdt2mentioning

confidence: 99%

CD Tools -- Condensed Detachment and Structure Generating Theorem Proving (System Description)

Wernhard¹

2022

Preprint

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CD Tools is a Prolog library for experimenting with condensed detachment in first-order ATP, which puts a recent formal view centered around proof structures into practice. From the viewpoint of first-order ATP, condensed detachment offers a setting that is relatively simple but with essential features and serious applications, making it attractive as a basis for developing and evaluating novel techniques. CD Tools includes specialized provers based on the enumeration of proof structures. We focus here on one of these, SGCD, which permits to blend goal-and axiom-driven proof search in particularly flexible ways. In purely goal-driven configurations it acts similarly to a prover of the clausal tableaux or connection method family. In blended configurations its performance is much stronger, close to state-of-the-art provers, while emitting relatively short proofs. Experiments show characteristics and application possibilities of the structure generating approach realized by that prover. For a historic problem often studied in ATP it produced a new proof that is much shorter than any known one.

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“…In recent years, machine learning (ML) and neural methods have been increasingly used to guide the search procedures of automated theorem provers (ATPs). Such methods have been so far developed for choosing inferences in connection tableaux systems [50,27,29,37,51], resolution/superposition-based systems [24,23,20,49], SAT solvers [48], tactical ITPs [17,3,5,18,30,42,40] and most recently also for the iProver [31] instantiation-based system [9]. In SMT (Satisfiability Modulo Theories), ML has so far been mainly used for tasks such as portfolio and strategy optimization [47,36,2].…”

Section: Introductionmentioning

confidence: 99%

First Experiments with Neural cvc5

Piepenbrock,

Janota,

Urban

et al.

EPiC Series in Computing

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The cvc5 solver is today one of the strongest systems for solving first order problems with theories but also without them. In this work we equip its enumeration-based instan- tiation with a neural network that guides the choice of the quantified formulas and their instances. For that we develop a relatively fast graph neural network that repeatedly scores all available instantiation options with respect to the available formulas. The network runs directly on a CPU without the need for any special hardware. We train the neural guidance on a large set of proofs generated by the e-matching instantiation strategy and evaluate its performance on a set of previously unseen problems.

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lazyCoP: Lazy Paramodulation Meets Neurally Guided Search

Cited by 8 publications

References 25 publications

gym-saturation: an OpenAI Gym environment for saturation provers

gym-saturation: an OpenAI Gym environment for saturation provers

CD Tools -- Condensed Detachment and Structure Generating Theorem Proving (System Description)

First Experiments with Neural cvc5

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