ModGen: theorem proving by model generation
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Automatic proofs and counterexamples for some ortholattice identities
Information Processing Letters
Automatic symmetry breaking method combined with SAT
Proceedings of the 2001 ACM symposium on Applied computing
Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
FINDER: Finite Domain Enumerator - System Description
CADE-12 Proceedings of the 12th International Conference on Automated Deduction
BerkMin: A Fast and Robust Sat-Solver
Proceedings of the conference on Design, automation and test in Europe
Automatic generation of some results in finite algebra
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
SEM: a system for enumerating models
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
An incremental answer set programming based system for finite model computation
JELIA'10 Proceedings of the 12th European conference on Logics in artificial intelligence
An incremental answer set programming based system for finite model computation
AI Communications - Answer Set Programming
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Finding countermodels is an effective way of disproving false conjectures. In first-order predicate logic, model finding is an undecidable problem. But if a finite model exists, it can be found by exhaustive search. The finite model generation problem in the first-order logic can also be translated to the satisfiability problem in the propositional logic. But a direct translation may not be very efficient. This paper discusses how to take the symmetries into account so as to make the resulting problem easier. A static method for adding constraints is presented, which can be thought of as an approximation of the least number heuristic (LNH). Also described is a dynamic method, which asks a model searcher like SEM to generate a set of partial models, and then gives each partial model to a propositional prover. The two methods are analyzed, and compared with each other.