[Tccc] JSTSP Special Issue on Learning-Based Decision Making in Dynamic Systems under Uncertainty
Qing Zhao
qzhaoatucdavis.edu
Fri Sep 28 22:31:13 EDT 2012
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Call for Papers
IEEE Signal Processing Society
IEEE Journal of Selected Topics in Signal Processing
Special Issue on Learning-Based Decision Making in Dynamic Systems under
Uncertainty
The design of dynamic systems for many emerging applications faces
increasing uncertainty: in cognitive radio systems for dynamic spectrum
access, secondary users need to detect and exploit temporally and spatially
varying spectrum white space under incomplete, inaccurate, and even unknown
models of spectrum occupancy, noise, and fading; in large-scale wireless
sensor networks with random deployment, low-cost battery-powered sensors
need to function collectively without assuming a priori knowledge of the
network topology or the communication environment; in cyber systems,
intrusion detection algorithms need to counter increasingly sophisticated
attacks that may not follow a well behaved stochastic model and may react
in real time to the detection scheme. In designing such dynamic systems,
learning becomes a crucial part of decision making; actions cannot be
predetermined but rather must adapt to past observations obtained through
interactions with the environment..
This special issue covers both theories and applications of learning-based
stochastic optimization and decision making. It focuses on learning and
decision-making techniques that emphasize the temporal dynamic nature of
the underlying system and adapt and improve over time through active
interactions with the system. Original unpublished contributions are
solicited in the following non-exhaustive list of topics.
- Stochastic optimization and control under incomplete, inaccurate, or
unknown models.
- Sequential decision making, adaptive control, Markov decision processes
under uncertainty.
- Reinforcement learning, Q-learning in dynamic systems.
- Stochastic online learning, multi-armed bandit problems.
- Sample-path-based learning, event-based sequential optimization.
- Distributed learning, control, and decision making.
- Learning and optimization under resource and computational constraints.
- Applications in various dynamic systems.
Prospective authors should visit
http://www.signalprocessingsociety.org/publications/periodicals/jstsp/
for information on paper submission. Manuscripts should be submitted using
the Manuscript Central system at
http://mc.manuscriptcentral.com/jstsp-ieee.
Manuscripts will be peer reviewed according to the standard IEEE process.
Manuscript submission due: Oct. 20, 2012
First review completed: Jan. 15, 2013
Revised manuscript due: Feb. 15, 2013
Second review completed: Apr. 1, 2013
Final manuscript due: Apr. 20, 2013
Lead guest editor:
Qing Zhao, University of California, Davis, USA (qz... at ucdavis.edu)
Guest editors:
Edwin Chong, Colorado State University, Fort Collins, USA
(edwin.ch... at colostate.edu)
Bhaskar Krishnamachari, University of Southern California, USA
(bkris... at usc.edu)
Amir Leshem, Bar-Ilan University, Israel (lesh... at eng.biu.ac.il)
Sean Meyn, University of Florida, USA (m... at ece.ufl.edu)
Venugopal V. Veeravalli, University of Illinois, Urbana-Champaign, USA
(v... at illinois.edu)
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