Noise strategies for improving local search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Experimental results on the crossover point in random 3-SAT
Artificial Intelligence - Special volume on frontiers in problem solving: phase transitions and complexity
A machine program for theorem-proving
Communications of the ACM
Constraints
Implementing the Davis–Putnam Method
Journal of Automated Reasoning
A Discrete Lagrangian-Based Global-SearchMethod for Solving Satisfiability Problems
Journal of Global Optimization
Constraint-Based Local Search
Applying GSAT to non-clausal formulas
Journal of Artificial Intelligence Research
Generation of hard non-clausal random satisfiability problems
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Evidence for invariants in local search
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Variable dependency in local search: prevention Is better than cure
SAT'07 Proceedings of the 10th international conference on Theory and applications of satisfiability testing
Programming for modular reconfigurable robots
Programming and Computing Software
Depth-driven circuit-level stochastic local search for SAT
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
On the violation of circuits in decomposable negation normal form
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
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Stochastic local search techniques have been successful in solving propositional satisfiability (SAT) problems encoded in conjunctive normal form (CNF). Recently complete solvers have shown that there are advantages to tackling propositional satisfiability problems in a more expressive natural representation, since the conversion to CNF can lose problem structure and introduce significantly more variables to encode the problem. In this work we develop a non-CNF SAT solver based on stochastic local search techniques. Crucially the system must be able to represent how true a proposition is and how false it is, as opposed to the usual stochastic methods which represent simply truth or degree of falsity (penalty). Our preliminary experiments show that on certain benchmarks the non-CNF local search solver can outperform highly optimized CNF local search solvers as well as existing CNF and non-CNF complete solvers.