An Ensemble Approach for Automated Theorem Proving Based on Efficient Name Invariant Graph Neural Representations Article Swipe
Achille Fokoue
,
Ibrahim Abdelaziz
,
Maxwell Crouse
,
Shajith Ikbal
,
Akihiro Kishimoto
,
J. G. R. Lima
,
Ndivhuwo Makondo
,
Radu Marinescu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2305.08676
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2305.08676
Using reinforcement learning for automated theorem proving has recently received much attention. Current approaches use representations of logical statements that often rely on the names used in these statements and, as a result, the models are generally not transferable from one domain to another. The size of these representations and whether to include the whole theory or part of it are other important decisions that affect the performance of these approaches as well as their runtime efficiency. In this paper, we present NIAGRA; an ensemble Name InvAriant Graph RepresentAtion. NIAGRA addresses this problem by using 1) improved Graph Neural Networks for learning name-invariant formula representations that is tailored for their unique characteristics and 2) an efficient ensemble approach for automated theorem proving. Our experimental evaluation shows state-of-the-art performance on multiple datasets from different domains with improvements up to 10% compared to the best learning-based approaches. Furthermore, transfer learning experiments show that our approach significantly outperforms other learning-based approaches by up to 28%.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2305.08676
- https://arxiv.org/pdf/2305.08676
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4376654233
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- OpenAlex ID
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https://openalex.org/W4376654233Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2305.08676Digital Object Identifier
- Title
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An Ensemble Approach for Automated Theorem Proving Based on Efficient Name Invariant Graph Neural RepresentationsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-05-15Full publication date if available
- Authors
-
Achille Fokoue, Ibrahim Abdelaziz, Maxwell Crouse, Shajith Ikbal, Akihiro Kishimoto, J. G. R. Lima, Ndivhuwo Makondo, Radu MarinescuList of authors in order
- Landing page
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https://arxiv.org/abs/2305.08676Publisher landing page
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https://arxiv.org/pdf/2305.08676Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2305.08676Direct OA link when available
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Invariant (physics), Computer science, Graph, Artificial intelligence, Artificial neural network, Theoretical computer science, Reinforcement learning, Representation (politics), Machine learning, Mathematics, Political science, Law, Mathematical physics, PoliticsTop concepts (fields/topics) attached by OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.evaluation | 124 |
| abstract_inverted_index.statements | 18, 28 |
| abstract_inverted_index.approaches. | 144 |
| abstract_inverted_index.efficiency. | 76 |
| abstract_inverted_index.experiments | 148 |
| abstract_inverted_index.outperforms | 154 |
| abstract_inverted_index.performance | 67, 127 |
| abstract_inverted_index.Furthermore, | 145 |
| abstract_inverted_index.experimental | 123 |
| abstract_inverted_index.improvements | 135 |
| abstract_inverted_index.transferable | 38 |
| abstract_inverted_index.reinforcement | 1 |
| abstract_inverted_index.significantly | 153 |
| abstract_inverted_index.learning-based | 143, 156 |
| abstract_inverted_index.name-invariant | 102 |
| abstract_inverted_index.RepresentAtion. | 88 |
| abstract_inverted_index.characteristics | 111 |
| abstract_inverted_index.representations | 15, 48, 104 |
| abstract_inverted_index.state-of-the-art | 126 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 8 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.41999998688697815 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile |