Improving generalization of neural SAT solvers through algorithmic reasoning
Combinatorial optimization problems present a significant challenge due to their inherent complexity, naturally exponential, making algorithms impractical. Of special interest is the Boolean satisfiability problem (SAT) due to its wide-ranging applications in software and hardware verification, arti...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/410881 |
| Acceso en línea: | https://hdl.handle.net/2117/410881 |
| Access Level: | acceso abierto |
| Palabra clave: | Combinatorial optimization Neural networks (Computer science) Machine learning Deep learning (Machine learning) Raonament Algorítmic Neuronal SAT Solvers Neuronals Problema de Satisfacibilitat Booleana Xarxes Neuronals per Grafs Xarxes Neuronals Aprenentatge de Representacions per Grafs Aprenentatge Profund Aprenentatge Automàtic Intel·ligència Artificial Optimització Combinatòria DPLL Algoritmes Grafs Neural Algorithmic Reasoning Neural SAT solvers Graph Neural Networks Graph Representation Learning Neural Networks Deep Learning Machine Learning Artificial Intelligence SAT solving Boolean Satisfiability Problem Algorithms Combinatorial Optimization Graphs Optimització combinatòria Xarxes neuronals (Informàtica) Aprenentatge automàtic Aprenentatge profund Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic |
| Sumario: | Combinatorial optimization problems present a significant challenge due to their inherent complexity, naturally exponential, making algorithms impractical. Of special interest is the Boolean satisfiability problem (SAT) due to its wide-ranging applications in software and hardware verification, artificial intelligence, cryptography and optimization. Leveraging the significant advances in deep learning, specially Graph Neural Networks (GNNs), neural combinatorial SAT solvers have emerged as a promising alternative for solving SAT. However, their limited generalization and extrapolation capabilities make them less competitive than classical solvers. In recent years, Neural Algorithmic Reasoning has gained traction by altering the conventional approach of how machines learn to solve tasks. Rather than just learning to map inputs to outputs, this method focuses on understanding and replicating the reasoning steps of an algorithm, inherently characterized by a strong generalization. In this work, we introduce novel GNN-based models that leverage neural algorithmic reasoning, and we empirically show their great potential to enhance their ability to extrapolate. This exploration offers a fresh perspective on how the synergy between deep neural networks and classic algorithmic reasoning, can benefit neural combinatorial optimization and SAT solving. |
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