The complexity of facets resolved
Journal of Computer and System Sciences - 26th IEEE Conference on Foundations of Computer Science, October 21-23, 1985
Abstract interpretation of reactive systems
ACM Transactions on Programming Languages and Systems (TOPLAS)
Patterns in property specifications for finite-state verification
Proceedings of the 21st international conference on Software engineering
Semantic Minimization of 3-Valued Propositional Formulae
LICS '02 Proceedings of the 17th Annual IEEE Symposium on Logic in Computer Science
The complexity of facets (and some facets of complexity)
STOC '82 Proceedings of the fourteenth annual ACM symposium on Theory of computing
Model Checking Vs. Generalized Model Checking: Semantic Minimizations for Temporal Logics
LICS '05 Proceedings of the 20th Annual IEEE Symposium on Logic in Computer Science
Efficient Patterns for Model Checking Partial State Spaces in CTL ∩ LTL
Electronic Notes in Theoretical Computer Science (ENTCS)
Complexity of decision problems for mixed and modal specifications
FOSSACS'08/ETAPS'08 Proceedings of the Theory and practice of software, 11th international conference on Foundations of software science and computational structures
Hi-index | 0.00 |
Partial Kripke structures model only parts of a state space and so enable aggressive abstraction of systems prior to verifying them with respect to a formula of temporal logic. This partiality of models means that verifications may reply with true (all refinements satisfy the formula under check), false (no refinement satisfies the formula under check) or don't know. Generalized model checking is the most precise verification for such models (all don't know answers imply that some refinements satisfy the formula, some don't), but computationally expensive. A compositional model-checking algorithm for partial Kripke structures is efficient, sound (all answers true and false are truthful), but may lose precision by answering don't know instead of a factual true or false. Recent work has shown that such a loss of precision does not occur for this compositional algorithm for most practically relevant patterns of temporal logic formulas. Formulas that never lose precision in this manner are called semantically self-minimizing. In this paper we provide a systematic study of the complexity of deciding whether a formula of propositional logic, propositional modal logic or the propositional modal mu-calculus is semantically self-minimizing.