CfP: Workshop on Verification, Validation and Certification of Neuro-Adaptive Controllers in Safety-Related Areas

Johann Schumann schumann at
Thu Mar 24 20:31:07 EST 2005

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          Verification, Validation and Certification of
       Neuro-Adaptive Controllers in Safety-Related Areas

                   in conjunction with IJCNN 2005
         International Joint Conference on Neural Networks
                        Montreal, Canada
                        August 5th, 2005

Important Dates
     Submission of paper/abstract:    4/27/2005
     Notification of acceptance:      5/4/2005
     Camera-ready copy:               5/15/2005
     Workshop:                        8/5/2005

Over the recent years, artificial Neural Networks (NNs) have found their 
into various safety-related and safety-critical areas, like transportation,
avionics, environmental monitoring and control, and medical applications.
Quite often, these applications (using NN techniques ranging from
classification to monitoring and control) proved to be highly successful,
leading from a pure research prototype into a serious experimental system
(e.g., a neural-network-based flight-control system test-flown on a manned
NASA F-15 aircraft) or a commercial product (e.g., Sharp's Logi-cook).
However, the general question of how to make sure that the NN-based 
control system performs as expected in all cases has not yet been addressed
satisfactorily. While theory and concepts of adaptive systems and 
control have been studied in depth over the past decade or so, only very
little attention has been paid to the issue of validating the correctness
and safety of their operation. All safety-related software applications
require careful verification and validation (V&V) of the software 
ranging from extended testing to full-fledged certification procedures
(e.g., DO178-B). The adaptive nature of neural networks requires a
significantly different approach to verification and validation than 
used for
traditional software, since dynamic adaptation of parameters, iterative
numerical algorithms, and complex control architectures renders traditional
approaches to V&V impracticable. Many prototypical/experimental 
application of
neural networks in safety-related areas have demonstrated superior behavior
and practical usefulness. Unless, however, methods and techniques have been
developed which are capable of assuring the correctness and performance 
of a
neural-network based system, NN applicability in safety-critical areas is
substantially limited.

The purpose of the workshop is to bring together researchers and users of
learning and adaptive systems and control systems in order to create a 
for discussing recent advances in verification, validation, and testing of
learning systems, to understand better the practical requirements for
developing and deploying neuro-adaptive, and to inspire research on new
methods and techniques for verification, validation, and testing.

Topics of interest include but are not limited to:
  * applications of learing and adaptive methods and NNs in safety-critical
    areas and experience/lessons learned.
  * applications of collaborative filtering problems, node modeling for 
    networks and dependency networks, sequential decision making tasks,
    diagnosis problems, autonomous systems, robotics, and security, etc.
  * techniques, tools, and methods to assess and guarantee the 
performance of
    a NN, e.g., statistical (Bayesian) methods, rule extraction with 
    V&V, methods for convergence/stability analysis, dynamic monitoring 
of the
    NN behavior, etc.,
  * V&V techniques that are specifically suitable for on-line trained and
    adaptive systems, and
  * software development, V&V, and certification processes for learning and
    adaptive systems.

Organizing committee
      Johann Schumann, RIACS/NASA Ames,  schumann at
      Pramod Gupta, QSS/NASA Ames, pgupta at
      Dragos Margineantu, The Boeing Company, 
dragos.d.margineantu at

Program committee
      B. Cukic, WVU
      S. Jacklin, NASA Ames
      T. Menzies, PSU
      A. Mili, NJIT
      M. Richard, NASA DFRC
      F. Sheldon, ORNL


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