[1] Yean-Ru Chen Chin-Wei Lien. A hardware sat solver with efficient bcp. Masters thesis, National Cheng Kung University, 2019.
[2] Stephen A Cook. The complexity of theorem-proving procedures. In Proceedings of the third annual ACM symposium on Theory of computing, pages 151–158, 1971.
[3] Martin Davis, George Logemann, and Donald Loveland. A machine program for
theorem-proving. Communications of the ACM, 5(7):394–397, 1962.
[4] Richard Evans, David Saxton, David Amos, Pushmeet Kohli, and Edward Grefenstette. Can neural networks understand logical entailment? arXiv preprint arXiv:1802.08535, 2018.
[5] Carla P Gomes and Bart Selman. Algorithm portfolios. Artificial Intelligence, 126(1-2):43–62, 2001.
[6] Holger H Hoos and Thomas Stützle. Stochastic local search: Foundations and applications. Elsevier, 2004.
[7] Samuel H Huang. Supervised feature selection: A tutorial. Artif. Intell. Res., 4(2):22–37, 2015.
[8] Frank Hutter, Holger H Hoos, Kevin Leyton-Brown, and Thomas Stützle. Paramils: an automatic algorithm configuration framework. Journal of Artificial Intelligence Research,36:267–306, 2009.
[9] Michail G Lagoudakis and Michael L Littman. Learning to select branching rules in the dpll procedure for satisfiability. Electronic Notes in Discrete Mathematics, 9:344–359, 2001.
[10] D Mitchell, B Selman, and H Leveque. A new method for solving hard satisfiability problems. In Proceedings of the tenth national conference on artificial intelligence (AAAI-92), pages 440–446, 1992.
[11] Mladen Nikolić, Filip Marić, and Predrag Janičić. Instance-based selection of policies for sat solvers. In International Conference on Theory and Applications of Satisfiability Testing, pages 326–340. Springer, 2009.
[12] Mladen Nikolić, Filip Marić, and Predrag Janičić. Simple algorithm portfolio for sat. Artificial Intelligence Review, 40(4):457–465, 2013.
[13] Eugene Nudelman, Kevin Leyton-Brown, Holger H Hoos, Alex Devkar, and Yoav Shoham. Understanding random sat: Beyond the clauses-to-variables ratio. In International Conference on Principles and Practice of Constraint Programming, pages 438–452. Springer, 2004.
[14] Paul Walton Purdom, Jr and Cynthia A Brown. The pure literal rule and polynomial average time. SIAM Journal on Computing, 14(4):943–953, 1985.
[15] Philip Sedgwick. Pearson＇s correlation coefficient. Bmj, 345, 2012.
[16] Bart Selman, Henry A Kautz, and Bram Cohen. Noise strategies for improving local search. In AAAI, volume 94, pages 337–343, 1994.
[17] Daniel Selsam, Matthew Lamm, Benedikt Bünz, Percy Liang, Leonardo de Moura, and David L Dill. Learning a sat solver from single-bit supervision. arXiv preprint arXiv:1802.03685, 2018.
[18] Yean-Ru Chen Shi-Wei Wu. A non-exhaustive hardware sat solver with deeper state exploration capability. Master’s thesis, National Cheng Kung University, 2019.
[19] Dave AD Tompkins and Holger H Hoos. Ubcsat: An implementation and experimentation environment for sls algorithms for sat and max-sat. In International conference on theory and applications of satisfiability testing, pages 306–320. Springer, 2004.
[20] Dave AD Tompkins, Frank Hutter, and Holger H Hoos. Scaling and probabilistic
smoothing (saps). SAT, 2004.
[21] Lin Xu, Frank Hutter, Holger H Hoos, and Kevin Leyton-Brown. Satzilla: portfoliobased algorithm selection for sat. Journal of artificial intelligence research, 32:565–606, 2008.
[22] Lin Xu, Frank Hutter, Holger H Hoos, and Kevin Leyton-Brown. Satzilla2009: an
automatic algorithm portfolio for sat. SAT, 4:53–55, 2009.