LOFLMay 3

Knowledge Compilation for Quantification in Alternating Automata

arXiv:2605.0201732.1
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This work addresses the inefficiency of complementation in nondeterministic automata for verification tasks like QPTL satisfiability and HyperLTL model checking, offering a more practical approach.

The paper introduces a knowledge compilation approach for existential and universal quantification in alternating safety automata, enabling efficient quantifier elimination without complementation. The BDD-based prototype shows practical effectiveness on QPTL satisfiability benchmarks.

We present a knowledge compilation approach for existential and universal quantification in alternating automata. Knowledge compilation transforms formulas into normal forms with special properties that enable efficient answering of questions of interest. For Boolean formulas, several normal forms that have proven effective for existential/universal quantification, and even for functional synthesis, have been studied in the literature. For infinite word automata, quantification is a fundamental operation in verification tasks such as QPTL satisfiability checking and HyperLTL model checking. Existing algorithms rely on nondeterministic infinite word automata, where existential projection can be efficiently performed state-wise, but universal projection requires complementation. Complementing nondeterministic infinite word automata, however, is expensive in practice, making existing algorithms infeasible for automata in practice. Towards addressing this problem, we propose novel knowledge compilation techniques for existential and universal quantification on alternating safety automata. Our approach compiles alternating automata into normal forms where projection can be applied uniformly and efficiently to each state's transition function. Using the compilations for each type of quantification, we can effectively eliminate a sequence of alternating quantifiers in formulas without complementation. Our BDD-based prototype demonstrates the practical effectiveness of our algorithms on a suite of QPTL satisfiability benchmarks.

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