Maude as a Library: An Efficient All-Purpose Programming Interface

Rubio, Rubén García

doi:10.1007/978-3-031-12441-9_14

Cited by 12 publications

(3 citation statements)

References 30 publications

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“…Our implementation is based on Python with the Z3 SMT solver and Maude integrated using Python bindings [15] as depicted in Fig. 4.…”

Section: Implementation and Experimentsmentioning

confidence: 99%

Incremental Rewriting Modulo SMT

Whitters,

Nigam,

Talcott

2023

Lecture Notes in Computer Science

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Rewriting Modulo SMT combines two powerful automated deduction techniques (1) rewriting and (2) SMT-solving. Rewriting enables the specification of behavior of systems using rewriting rules, while SMT theories specify system properties. Rewriting Modulo SMT is enabled by combining existing tools, such as Maude and SMT solvers. Search algorithms used for carrying out Rewriting Modulo SMT, however, cannot exploit the incremental solving features available in SMT solvers as they are based on breadth-first search. This paper addresses this limitation by proposing Incremental Rewriting Modulo SMT Theories, which is a syntactical restriction to rewriting rules. This restriction turns out to naturally be used in several applications of Rewriting Modulo SMT, including the verification of algorithms, cyber-physical systems, and security protocols. Moreover, we propose a Hybrid-Search algorithm for Incremental Rewriting Modulo SMT Theories that combines breadth-first search and depth-first search, thus enabling incremental SMT-solving. We demonstrate through a collection of existing benchmarks that the Hybrid-Search algorithm can achieve a 10 times performance improvement in verification times.

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“…Our implementation is based on Python with the Z3 SMT solver and Maude integrated using Python bindings [15] as depicted in Fig. 4.…”

Section: Implementation and Experimentsmentioning

confidence: 99%

Incremental Rewriting Modulo SMT

Whitters,

Nigam,

Talcott

2023

Lecture Notes in Computer Science

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“…The constraints in the traces (non-linear arithmetic formulas) are solved by the SMT-solver (Z3 [13]). We implemented the connection between the symbolic soft-agents libraries and SMT solvers using the Python Binders described in [29], thus enabling easy extensions to additional solvers and other tools in the future.…”

Section: Automating Recoverability Proofs Using Symbolic Soft-agentsmentioning

confidence: 99%

Automating Safety Proofs About Cyber-Physical Systems Using Rewriting Modulo SMT

Nigam

Talcott

2022

Lecture Notes in Computer Science

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Cyber-physical systems (CPSes), such as autonomous vehicles, use sophisticated components like ML-based controllers. It is difficult to provide evidence about the safe functioning of such components. To overcome this problem, Runtime Assurance Architecture (RTA) solutions have been proposed. The RTA's decision component evaluates the system's safety risk and whenever the risk is higher than acceptable the RTA switches to a safety mode that, for example, activates a controller with strong evidence for its safe functioning. In this way, RTAs increase CPS runtime safety and resilience by recovering the system from higher to lower risk levels. The goal of this paper is to automate recovery proofs of CPSes using RTAs. We first formalize the key verification problems, namely, the decision sampling-time adequacy problem and the time-bounded recoverability problem. We then demonstrate how to automatically generate proofs for the proposed verification problems using symbolic rewriting modulo SMT. Automation is enabled by integrating the rewriting logic tool (Maude), which generates sets of non-linear constraints, with an SMT-solver (Z3) to produce proofs

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“…This way, we verified how the search space expands for provable and non-provable formulae. Then, to experiment with different search strategies, we implemented the search stack using the Maude Python library [24]. This way, we could introduce programmatic control over the search space stack.…”

Section: Comparing Proof Search Performancementioning

confidence: 99%

Deep Inference in Proof Search: The Need for Shallow Inference

Kahramanogullari

EPiC Series in Computing

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Deep inference is a proof theoretical formalism that generalises the “shallow inference” of sequent calculus by permitting the application of inference rules on subformulae like term rewriting rules. Deep inference makes it possible to build shorter proofs than sequent calculus proofs. However, deep inference in proof search introduces higher nondeterminism, an obstacle in front of applications. Deep inference is essential for designing system BV, an extension of multiplicative linear logic (MLL) with a self-dual non-commutative operator. MLL has shallow inference systems, whereas BV is impossible with a shallow-only system. As Tiu showed, any restriction on rule depth makes a system incomplete for BV. This paper shows that any restriction that rules out shallow rules makes the system incomplete, too. Our results indicate that for system BV, shallow and deep rules must coexist for completeness. We provide extensive empirical evidence that deep inference can still be faster than shallow inference when used strategically with a proof theoretical technique for reducing nondeterminism. We show that prioritising deeper rule instances, in general, reduces the cost of proof search by reducing the size of the managed contexts, consequently providing more immediate access to shorter proofs. Moreover, we identify a class of MLL formulae with deep inference proof search times that grow linearly in the number of atoms in contrast to an exponential growth pattern with shallow inference. We introduce a large and exhaustive benchmark for MLL, with and without mix, and a proof search framework to apply various search strategies, which should be of independent interest.

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Maude as a Library: An Efficient All-Purpose Programming Interface

Cited by 12 publications

References 30 publications

Incremental Rewriting Modulo SMT

Incremental Rewriting Modulo SMT

Automating Safety Proofs About Cyber-Physical Systems Using Rewriting Modulo SMT

Deep Inference in Proof Search: The Need for Shallow Inference

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