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Failures in complex systems controlled by human operators can be difficult to anticipate because of unexpected interactions between the elements that compose the system, including human-automation interaction (HAI). HAI analyses would benefit from techniques that support investigating the possible combinations of system conditions and HAIs that might result in failures. Formal verification is a powerful technique used to mathematically prove that an appropriately scaled model of a system does or does not exhibit desirable properties. This paper discusses how formal verification has been used to evaluate HAI. It has been used to evaluate human-automation interfaces for usability properties and to find potential mode confusion. It has also been used to evaluate system safety properties in light of formally modeled task analytic human behavior. While capable of providing insights into problems associated with HAI, formal verification does not scale as well as other techniques such as simulation. However, advances in formal verification continue to address this problem, and approaches that allow it to complement more traditional analysis methods can potentially avoid this limitation.

In previous work, we proposed a "saturation" algorithm for symbolic state-space generation characterized by the use of multi-valued decision diagrams, boolean Kronecker operators, event locality, and a special iteration strategy. This approach outperforms traditional BDDbased techniques by several orders of magnitude in both space and time but, like them, assumes a priori knowledge of each submodel's state space. We introduce a new algorithm that merges explicit local statespace discovery with symbolic global state-space generation. This relieves the modeler from worrying about the behavior of submodels in isolation.