Abstract
A major hurdle in the algorithmic verification and control of systems is the need to find suitable abstract models, which omit enough details to overcome the state-explosion problem, but retain enough details to exhibit satisfaction or controllability with respect to the specification. The paradigm of counterexample-guided abstraction refinement suggests a fully automatic way of finding suitable abstract models: one starts with a coarse abstraction, attempts to verify or control the abstract model, and if this attempt fails and the abstract counterexample does not correspond to a concrete counterexample, then one uses the spurious counterexample to guide the refinement of the abstract model. We present a counterexample-guided refinement algorithm for solving ω-regular control objectives. The main difficulty is that in control, unlike in verification, counterexamples are strategies in a game between system and controller. In the case that the controller has no choices, our scheme subsumes known counterexample-guided refinement algorithms for the verification of ω-regular specifications. Our algorithm is useful in all situations where ω-regular games need to be solved, such as supervisory control, sequential and program synthesis, and modular verification. The algorithm is fully symbolic, and therefore applicable also to infinite-state systems.
This research was supported in part by the DARPA SEC grant F33615-C-98-3614, the ONR grant N00014-02-1-0671, and the NSF grants CCR-9988172, CCR-0085949, and CCR-0225610.
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Henzinger, T.A., Jhala, R., Majumdar, R. (2003). Counterexample-Guided Control. In: Baeten, J.C.M., Lenstra, J.K., Parrow, J., Woeginger, G.J. (eds) Automata, Languages and Programming. ICALP 2003. Lecture Notes in Computer Science, vol 2719. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45061-0_69
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