Higher-order Boltzmann machines
AIP Conference Proceedings 151 on Neural Networks for Computing
A satisfiability tester for non-clausal propositional calculus
Information and Computation
Simulated annealing: theory and applications
Simulated annealing: theory and applications
Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
Algorithms for testing the satisfiability of propositional formulae
Journal of Logic Programming
A Computing Procedure for Quantification Theory
Journal of the ACM (JACM)
Boltzmann Machines and their Applications
Proceedings of the Parallel Architectures and Languages Europe, Volume I: Parallel Architectures PARLE
Experiments of Fast Learning with High Order Boltzmann Machines
Applied Intelligence
Expert System Hardware for Fault Detection
Applied Intelligence
Empirical study of Q-learning based elemental hose transport control
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
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Boltzmann machines (BMs) are proposed as a computational model for the solution of the satisfiability (SAT) problem in the propositional calculus setting. Conditions that guarantee consensus function maxima for configurations of the BM associated with solutions to the satisfaction problem are given. Experimental results that show a linear behavior of BMs solving the satisfiability problem are presented and discussed.