General Method for Solving Four Types of SAT Problems

doi:10.48550/arXiv.1903.04671

D. Selsam, N. Bjørner, Guiding high-performance SAT solvers with unsat-core predictions , in: Theory and Applications of Satisfiability Testing–SAT 2019: 22nd International Conference, SAT 2019, Lisbon, Portugal, July 9–12, 2019, Proceedings 22, Springer, 2019, pp. 336–353 · 1903

Earlier work this paper cites.

doi:10.1016/0890-5401(91)90075-D

S. R. Buss, L. Hay, On truth-table reducibility to SAT , Information and Computation 91 (1) (1991) 86–102 · 1991

Earlier work this paper cites.

doi:10.1016/S0890-5401(03)00037-3

L. M. Kirousis, P. G. Kolaitis, The complexity of minimal satisfiability problems , Information and Computation 187 (1) (2003) 20–39 · 2003

Earlier work this paper cites.

doi:10.1007/11871842_29

L. Kocsis, C. Szepesvári, Bandit based monte-carlo planning , in: European conference on machine learning, Springer, 2006, pp. 282–293 · 2006

Earlier work this paper cites.

doi:10.1007/978-3-642-29178-4_26

D. Perez, P. Rohlfshagen, S. M. Lucas, Monte-Carlo tree search for the physical travelling salesman problem , in: European Conference on the Applications of Evolutionary Computation, Springer, 2012, pp. 255–264 · 2012

Earlier work this paper cites.

doi:10.1016/j.ic.2013.08.008

G. Gutin, M. Jones, D. Scheder, A. Yeo, A new bound for 3-satisfiable MaxSat and its algorithmic application , Information and Computation 231 (2013) 117–124 · 2013

Earlier work this paper cites.

doi:10.1016/j.ic.2013.02.002

Testing probabilistic equivalence through reinforcement learning , author=Desharnais, Josée and Laviolette, François and Zhioua, Sami, in: International Conference on Foundations of Software Technology and Theoretical Computer Science, Springer, 2006, pp. 236–247 · 2013

Earlier work this paper cites.

doi:10.1016/j.ic.2014.12.008

H. L. Bodlaender, M. Cygan, S. Kratsch, J. Nederlof, Deterministic single exponential time algorithms for connectivity problems parameterized by treewidth , Information and Computation 243 (2015) 86–111 · 2014

Earlier work this paper cites.

doi:10.1016/j.ins.2014.10.045

N. R. Sabar, G. Kendall, Population based Monte Carlo tree search hyper-heuristic for combinatorial optimization problems , Information Sciences 314 (2015) 225–239 · 2014

Earlier work this paper cites.

doi:10.1038/nature16961

D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, et al., Mastering the game of Go with deep neural networks and tree search , nature 529 (7587) (2016) 484–489 · 2016

Earlier work this paper cites.

doi:10.1007/978-3-319-44953-1_17

J. Goffinet, R. Ramanujan, Monte-carlo tree search for the maximum satisfiability problem , in: International Conference on Principles and Practice of Constraint Programming, Springer, 2016, pp. 251–267 · 2016

Earlier work this paper cites.

doi:10.1016/j.ic.2017.11.002

I. Kanj, D. M. Thilikos, G. Xia, On the parameterized complexity of monotone and antimonotone weighted circuit satisfiability , Information and Computation 257 (2017) 139–156 · 2017

Earlier work this paper cites.

doi:10.1038/nature24270

D. Silver, J. Schrittwieser, K. Simonyan, I. Antonoglou, A. Huang, A. Guez, T. Hubert, L. Baker, M. Lai, A. Bolton, et al., Mastering the game of go without human knowledge , nature 550 (7676) (2017) 354–359 · 2017

Earlier work this paper cites.

J. Schloeter, A Monte Carlo tree search based conflict-driven clause learning SAT solver , INFORMATIK 2017 doi:10.18420/in2017_257

2017

Cited alongside, same era.

S. Amizadeh, S. Matusevych, M. Weimer, Learning to solve circuit-sat: An unsupervised differentiable approach , in: International Conference on Learning Representations, 2018

2018

Cited alongside, same era.

doi:10.1007/978-3-030-16194-1_9

T. Guo, C. Han, S. Tang, M. Ding, Solving combinatorial problems with machine learning methods , in: Nonlinear Combinatorial Optimization, Springer, 2019, pp. 207–229 · 2019

Cited alongside, same era.

doi:10.1007/978-3-030-16194-1_9

T. Guo, C. Han, S. Tang, Machine Learning Methods for Combinatorial Optimization (in Chinese) , Beijing: Science Press, 2019 · 2019

Cited alongside, same era.

V. Kurin, S. Godil, S. Whiteson, B. Catanzaro, Can Q-learning with graph networks learn a generalizable branching heuristic for a SAT solver? , Advances in Neural Information Processing Systems 33 (2020) 9608–9621

2020

doi:https://doi.org/10.1016/j.cor.2021.105400

N. Mazyavkina, S. Sviridov, S. Ivanov, E. Burnaev, Machine Learning Methods for Combinatorial Optimization , Computers & Operations Research 134 (2021) 105400 · 2021

Later among the works it cites.

doi:https://doi.org/10.1016/j.cor.2021.105357

R. Gama, H. L. Fernandes, A reinforcement learning approach to the orienteering problem with time windows , Computers & Operations Research 133 (2021) 105357 · 2021

Later among the works it cites.

doi:10.1145/3477911.3477915

M. S. Kiarostami, M. Daneshvaramoli, S. Khalaj Monfared, A. Visuri, H. Karisani, S. Hosio, H. Khashehchi, E. Futuhi, D. Rahmati, S. Gorgin, On Using Monte-Carlo Tree Search to Solve Puzzles , in: 2021 7th International Conference on Computer Technology Applications, 2021, pp. 18–26 · 2021

Later among the works it cites.

arXiv:2110.15105

Original

C. Wang, Y. Yang, O. Slumbers, C. Han, T. Guo, H. Zhang, J. Wang, A Game-Theoretic Approach for Improving Generalization Ability of TSP Solvers , in: ICLR 2022 Workshop on Gamification and Multiagent Solutions, 2022 · 2022

Later among the works it cites.

C. Wang, C. Han, T. Guo, M. Ding, Solving uncapacitated P-Median problem with reinforcement learning assisted by graph attention networks , Applied Intelligence (2022) 1–16 doi:10.1007/s10489-022-03453-z