New SEER method uses ML to optimize energetic reasoning in constraint programming

Researchers have developed a novel approach called SEER, which utilizes supervised machine learning to optimize the use of energetic reasoning propagators in constraint programming. This method aims to balance the computational cost of propagation with its effectiveness in reducing search spaces. By training an oracle function, SEER can intelligently decide when to employ complex propagators, offering flexibility and potential integration into existing solvers. Experiments indicate high prediction accuracy and provide insights into feature selection for building such an oracle. AI

IMPACT This research could lead to more efficient AI solvers by optimizing computational resource allocation.

RANK_REASON The cluster contains a research paper detailing a new method for constraint programming. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• alphaXiv
• arXiv
• CatalyzeX
• constraint programming
• DagsHub
• Energetic Reasoning
• Gotit.pub
• Hugging Face
• machine learning
• ScienceCast
• Seer

• paper
• model release

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