27,004 research outputs found
Applying Formal Methods to Networking: Theory, Techniques and Applications
Despite its great importance, modern network infrastructure is remarkable for
the lack of rigor in its engineering. The Internet which began as a research
experiment was never designed to handle the users and applications it hosts
today. The lack of formalization of the Internet architecture meant limited
abstractions and modularity, especially for the control and management planes,
thus requiring for every new need a new protocol built from scratch. This led
to an unwieldy ossified Internet architecture resistant to any attempts at
formal verification, and an Internet culture where expediency and pragmatism
are favored over formal correctness. Fortunately, recent work in the space of
clean slate Internet design---especially, the software defined networking (SDN)
paradigm---offers the Internet community another chance to develop the right
kind of architecture and abstractions. This has also led to a great resurgence
in interest of applying formal methods to specification, verification, and
synthesis of networking protocols and applications. In this paper, we present a
self-contained tutorial of the formidable amount of work that has been done in
formal methods, and present a survey of its applications to networking.Comment: 30 pages, submitted to IEEE Communications Surveys and Tutorial
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Neurons and symbols: a manifesto
We discuss the purpose of neural-symbolic integration including its principles, mechanisms and applications. We outline a cognitive computational model for neural-symbolic integration, position the model in the broader context of multi-agent systems, machine learning and automated reasoning, and list some of the challenges for the area of
neural-symbolic computation to achieve the promise of effective integration of robust learning and expressive reasoning under uncertainty
Model-based dependability analysis : state-of-the-art, challenges and future outlook
Abstract: Over the past two decades, the study of model-based dependability analysis has gathered significant research interest. Different approaches have been developed to automate and address various limitations of classical dependability techniques to contend with the increasing complexity and challenges of modern safety-critical system. Two leading paradigms have emerged, one which constructs predictive system failure models from component failure models compositionally using the topology of the system. The other utilizes design models - typically state automata - to explore system behaviour through fault injection. This paper reviews a number of prominent techniques under these two paradigms, and provides an insight into their working mechanism, applicability, strengths and challenges, as well as recent developments within these fields. We also discuss the emerging trends on integrated approaches and advanced analysis capabilities. Lastly, we outline the future outlook for model-based dependability analysis
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
The xSAP Safety Analysis Platform
This paper describes the xSAP safety analysis platform. xSAP provides several
model-based safety analysis features for finite- and infinite-state synchronous
transition systems. In particular, it supports library-based definition of
fault modes, an automatic model extension facility, generation of safety
analysis artifacts such as Dynamic Fault Trees (DFTs) and Failure Mode and
Effects Analysis (FMEA) tables. Moreover, it supports probabilistic evaluation
of Fault Trees, failure propagation analysis using Timed Failure Propagation
Graphs (TFPGs), and Common Cause Analysis (CCA). xSAP has been used in several
industrial projects as verification back-end, and is currently being evaluated
in a joint R&D Project involving FBK and The Boeing Company
On the Verification of a WiMax Design Using Symbolic Simulation
In top-down multi-level design methodologies, design descriptions at higher
levels of abstraction are incrementally refined to the final realizations.
Simulation based techniques have traditionally been used to verify that such
model refinements do not change the design functionality. Unfortunately, with
computer simulations it is not possible to completely check that a design
transformation is correct in a reasonable amount of time, as the number of test
patterns required to do so increase exponentially with the number of system
state variables. In this paper, we propose a methodology for the verification
of conformance of models generated at higher levels of abstraction in the
design process to the design specifications. We model the system behavior using
sequence of recurrence equations. We then use symbolic simulation together with
equivalence checking and property checking techniques for design verification.
Using our proposed method, we have verified the equivalence of three WiMax
system models at different levels of design abstraction, and the correctness of
various system properties on those models. Our symbolic modeling and
verification experiments show that the proposed verification methodology
provides performance advantage over its numerical counterpart.Comment: In Proceedings SCSS 2012, arXiv:1307.802
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