Deep learning for the generation of heuristics in answer set programming: a case study of graph coloring

DOI10.1007/978-3-031-15707-3_12OpenAlexW4293179858MaRDI QIDQ2694562

Carmine Dodaro, Francesco Ricca, Luca Oneto, Davide Ilardi

Publication date: 4 April 2023

Full work available at URL: https://doi.org/10.1007/978-3-031-15707-3_12

zbMATH Keywords

heuristicsgraph coloringanswer set programmingdeep learning

Mathematics Subject Classification ID

Cellular automata (computational aspects) (68Q80)

Uses Software

• MiniSat
• WASP
• claspfolio 2
• Adam

Cites Work

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• Design and results of the Fifth Answer Set Programming Competition
• Machine learning-based restart policy for CDCL SAT solvers
• Multilayer feedforward networks are universal approximators
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• Advances in WASP
• Improving Coordinated SMT-Based System Synthesis by Utilizing Domain-Specific Heuristics
• Combining Answer Set Programming and domain heuristics for solving hard industrial problems (Application Paper)
• Efficiently Coupling the I-DLV Grounder with ASP Solvers
• Model Selection and Error Estimation in a Nutshell
• Theory and Applications of Satisfiability Testing
• Learning representations by back-propagating errors
• Understanding Machine Learning