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IsomorphFree Model Enumeration: A New Method for Checking Relational Specifications
 ACM TRANSACTIONS ON PROGRAMMING LANGUAGES AND SYSTEMS
, 1998
"... This article describes a technique for analyzing relational specifications. The underlying idea is very simple. Both simulation and checking amount to finding models of a relational formula, i.e., assignments for which the formula is true. For simulation the formula is the description of the operati ..."
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Cited by 18 (10 self)
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This article describes a technique for analyzing relational specifications. The underlying idea is very simple. Both simulation and checking amount to finding models of a relational formula, i.e., assignments for which the formula is true. For simulation the formula is the description of the operation; for checking, the formula is the negation of an assertion about an operation. Models are found by a generateandtest strategy: the formula is repeatedly evaluated for a series of assignments until one is found for which the formula is true
www.elsevier.com/locate/entcs Reducing Symmetries to Generate Easier SAT Instances 1
"... Finding countermodels is an effective way of disproving false conjectures. In firstorder 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 firstorder logic can also be translated ..."
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Cited by 1 (0 self)
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Finding countermodels is an effective way of disproving false conjectures. In firstorder 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 firstorder 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.
Improving firstorder model searching by propositional reasoning and lemma learning
 Proc. 7th Intâ€™l Conf. on Theory and Applications of Satisfiability Testing
, 2004
"... Abstract. The finite model generation problem in the firstorder logic is a generalization of the propositional satisfiability (SAT) problem. An essential algorithm for solving the problem is backtracking search. In this paper, we show how to improve such a search procedure by lemma learning. For ef ..."
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Cited by 1 (1 self)
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Abstract. The finite model generation problem in the firstorder logic is a generalization of the propositional satisfiability (SAT) problem. An essential algorithm for solving the problem is backtracking search. In this paper, we show how to improve such a search procedure by lemma learning. For efficiency reasons, we represent the lemmas by propositional formulas and use a SAT solver to perform the necessary reasoning. We have extended the firstorder model generator SEM, combining it with the SAT solver SATO. Experimental results show that the search time may be reduced significantly on many problems. 1