Validity of contextual formulas (extended version)

TL;DR

This paper investigates the validity of contextual formulas in modal $oldsymbol{ extmu}$-calculus, CTL, and LTL, proving EXP-completeness and providing an efficient automated tool for verifying contextual equivalences.

Contribution

It establishes the EXP-completeness of validity for contextual formulas and introduces an automated method that significantly outperforms manual proofs.

Findings

Validity of contextual formulas is EXP-complete.

A canonical context reduces validity checking to a single case.

Automated tool verifies LTL equivalences in milliseconds.

Abstract

Many well-known logical identities are naturally written as equivalences between contextual formulas. A simple example is the Boole-Shannon expansion $c[p] \equiv (p \wedge c[\mathrm{true}] ) \vee (\neg\, p \wedge c[\mathrm{false}] )$, where $c$ denotes an arbitrary formula with possibly multiple occurrences of a "hole", called a context, and $c[\varphi]$ denotes the result of "filling" all holes of $c$ with the formula $\varphi$. Another example is the unfolding rule $\mu X. c[X] \equiv c[\mu X. c[X]]$ of the modal $\mu$-calculus. We consider the modal $\mu$-calculus as overarching temporal logic and, as usual, reduce the problem whether $\varphi_1 \equiv \varphi_2$ holds for contextual formulas $\varphi_1, \varphi_2$ to the problem whether $\varphi_1 \leftrightarrow \varphi_2$ is valid . We show that the problem whether a contextual formula of the $\mu$-calculus is valid for all contexts can be reduced to validity of ordinary formulas. Our first result constructs a canonical context such that a formula is valid for all contexts if{}f it is valid for this particular one. However, the ordinary formula is exponential in the nesting-depth of the context variables. In a second result we solve this problem, thus proving that validity of contextual formulas is EXP-complete, as for ordinary equivalences. We also prove that both results hold for CTL and LTL as well. We conclude the paper with some experimental results. In particular, we use our implementation to automatically prove the correctness of a set of six contextual equivalences of LTL recently introduced by Esparza et al. for the normalization of LTL formulas. While Esparza et al. need several pages of manual proof, our tool only needs milliseconds to do the job and to compute counterexamples for incorrect variants of the equivalences.