Satisfiabilitybased

Satisfiabilitybased refers to methods and approaches that solve problems primarily by using propositional satisfiability (SAT) solvers. In satisfiabilitybased workflows, the target problem is translated into a propositional formula, often in conjunctive normal form (CNF), and the SAT solver is used to decide satisfiability and, in many cases, extract a model. This paradigm leverages the efficiency and maturity of modern SAT technology, including advanced search heuristics and conflict-driven clause learning.

The term is widely used across fields such as formal verification, model checking, hardware and software verification, planning, and constraint solving. In formal methods, bounded model checking and symbolic model checking frequently rely on SAT encodings to explore large state spaces. In artificial intelligence, SAT-based planning encodes actions, states, and goals as logical variables and clauses, turning plan existence into a satisfiability problem. Constraint solving and scheduling problems are also often cast as SAT or SAT-based optimization problems.

Techniques commonly associated with satisfiabilitybased methods include problem encoding strategies, incremental and assumption-based solving, and the use of SAT solvers with learning and backjumping capabilities. Extensions such as MaxSAT and pseudo-Boolean optimization provide ways to optimize objectives within the same framework. SAT-based approaches can be combined with theories in SMT (satisfiability modulo theories) to handle richer domains, though this moves beyond pure satisfiability alone.

Advantages of satisfiabilitybased methods include scalability to large, combinatorial problems and the ability to leverage decades of SAT solver research. Limitations include potential encoding complexity, the possibility of CNF blow-up, and difficulties representing certain theories directly without specialized solvers.