Abstract
We have employed a chosen set of machine learning models to solve the 3-CNF-SAT problem. Through f1-scores, we obtain how these algorithms perform at solving the problem as a classification task. The implication of this endeavour is exciting given the property of the NP-complete class problems being polynomial-time reducible to each other.
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References
D. Devlin, B. O’Sullivan, Satisfiability as a Classification Problem Grant No. 05/IN/I886 Cork Constraint Computation Centre Department of Computer Science, University College Cork, Ireland Supported by Science Foundation Ireland. http://www.cs.ucc.ie/~osullb/pubs/classification.pdf
D. Selsam, M. Lamm, B. Bunz, P. Liang, D.L. Dill, Learning a SAT Solver from Single-Bit Supervision Submitted on 11 Feb 2018, last revised 5 Jan 2019. Department of Computer Science Stanford University Stanford, CA 94305 and Microsoft Research Redmond, WA 98052. https://arxiv.org/pdf/1802.03685.pdf
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Atkari, A., Dhargalkar, N., Angne, H. (2020). Employing Machine Learning Models to Solve Uniform Random 3-SAT. In: Jain, L., Tsihrintzis, G., Balas, V., Sharma, D. (eds) Data Communication and Networks. Advances in Intelligent Systems and Computing, vol 1049. Springer, Singapore. https://doi.org/10.1007/978-981-15-0132-6_17
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DOI: https://doi.org/10.1007/978-981-15-0132-6_17
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