CALL FOR PAPERS
(my aplogies if you receive this multiple times)
WORKSHOP
on
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
way
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
adaptive
control system performs as expected in all cases has not yet been addressed
satisfactorily. While theory and concepts of adaptive systems and
intelligent
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
components,
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
forum
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
belief
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
subsequent
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 email.arc.nasa.gov
Pramod Gupta, QSS/NASA Ames, pgupta at email.arc.nasa.gov
Dragos Margineantu, The Boeing Company,
dragos.d.margineantu at boeing.com
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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