ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Inferring state constraints for domain-independent planning
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Knowlege in action: logical foundations for specifying and implementing dynamical systems
Knowlege in action: logical foundations for specifying and implementing dynamical systems
An Iterative Algorithm for Synthesizing Invariants
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Checking Safety by Inductive Generalization of Counterexamples to Induction
FMCAD '07 Proceedings of the Formal Methods in Computer Aided Design
Property persistence in the situation calculus
Artificial Intelligence
SAT-based model checking without unrolling
VMCAI'11 Proceedings of the 12th international conference on Verification, model checking, and abstract interpretation
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In dynamic systems, state constraints are formulas that hold in every reachable state. It has been shown that state constraints can be used to greatly reduce the planning search space. They are also useful in program verification. In this paper, we propose a sound but incomplete method for automatic verification and discovery of ∀*∃* state constraints for a class of action theories that include many planning benchmarks. Our method is formulated in the situation calculus, theoretically based on Skolemization and Herbrand Theorem, and implemented with SAT solvers. Basically, we verify a state constraint by strengthening it in a novel and smart way so that it becomes a state invariant. We experimented with the blocks world, logistics and satellite domains, and the results showed that, almost all known state constraints can be verified in a reasonable amount of time, and meanwhile succinct and intuitive related state constraints are discovered.