4,838 research outputs found
Abstraction and Learning for Infinite-State Compositional Verification
Despite many advances that enable the application of model checking
techniques to the verification of large systems, the state-explosion problem
remains the main challenge for scalability. Compositional verification
addresses this challenge by decomposing the verification of a large system into
the verification of its components. Recent techniques use learning-based
approaches to automate compositional verification based on the assume-guarantee
style reasoning. However, these techniques are only applicable to finite-state
systems. In this work, we propose a new framework that interleaves abstraction
and learning to perform automated compositional verification of infinite-state
systems. We also discuss the role of learning and abstraction in the related
context of interface generation for infinite-state components.Comment: In Proceedings Festschrift for Dave Schmidt, arXiv:1309.455
Verifying the Safety of a Flight-Critical System
This paper describes our work on demonstrating verification technologies on a
flight-critical system of realistic functionality, size, and complexity. Our
work targeted a commercial aircraft control system named Transport Class Model
(TCM), and involved several stages: formalizing and disambiguating requirements
in collaboration with do- main experts; processing models for their use by
formal verification tools; applying compositional techniques at the
architectural and component level to scale verification. Performed in the
context of a major NASA milestone, this study of formal verification in
practice is one of the most challenging that our group has performed, and it
took several person months to complete it. This paper describes the methodology
that we followed and the lessons that we learned.Comment: 17 pages, 5 figure
Formal Modeling of Connectionism using Concurrency Theory, an Approach Based on Automata and Model Checking
This paper illustrates a framework for applying formal methods techniques, which are symbolic in nature, to specifying and verifying neural networks, which are sub-symbolic in nature. The paper describes a communicating automata [Bowman & Gomez, 2006] model of neural networks. We also implement the model using timed automata [Alur & Dill, 1994] and then undertake a verification of these models using the model checker Uppaal [Pettersson, 2000] in order to evaluate the performance of learning algorithms. This paper also presents discussion of a number of broad issues concerning cognitive neuroscience and the debate as to whether symbolic processing or connectionism is a suitable representation of cognitive systems. Additionally, the issue of integrating symbolic techniques, such as formal methods, with complex neural networks is discussed. We then argue that symbolic verifications may give theoretically well-founded ways to evaluate and justify neural learning systems in the field of both theoretical research and real world applications
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