ELECTRICAL ENGINEERING DEPARTMENT 1 SPTM Expert Panel SPTM Expert Panel SPTM Expert Panel SPTM Expert Panel In Network Processing, Adaptation, and Learning In Network Processing, Adaptation, and Learning In Network Processing, Adaptation, and Learning In Network Processing, Adaptation, and Learning ICASSP 2011 ICASSP 2011 ICASSP 2011 ICASSP 2011 May 27, 2011 Prague, Czech Republic http://www.istockphoto.com/ A. H. Sayed
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In Network Processing, Adaptation, and LearningIn Network Processing, Adaptation, and LearningIn Network Processing, Adaptation, and LearningIn Network Processing, Adaptation, and Learning
``An agent-based model is a computerized simulation of a number of decision-makers (agents) and institutions, which interact through prescribed rules… Suchmodels do not rely on the assumption that the economy will move towardsa predetermined equilibrium state, as other models do. Instead, at any given time, each agent acts according to its current situation, the state of the world around it and the rules governing its behaviour. .. Agent-based simulations can handle a far wider range of nonlinear behaviour than conventional equilibrium models.’’
ELECTRICAL ENGINEERING DEPARTMENT 11
Cognitive networks consist of spatially distributed
SSP on dynamic graphs: Consensus, games, information flows,social networks. [Society, sensor networks]
High dimensional SP and sparsity: Concentration of measuretheorems e.g. Compressed Sensing – lots of papers in ICASSP;TSP, 2 special issues of JSTSP. [Machine Learning, Biomedical]
Financial time series and SP [Finance]
Smart grid – SP magazine, ICASSP special session [Energy]
Distributed Sensing, Decision Making and Control Cognitivesystems, social learning, Bioinspired SP, dynamical games.[Economics, defence, energy efficiency]
SP and stochastic simualation in life sciences (from Molecularbiology to biomedical imaging and instrumentation).nature.com website: Pfizer, Novartis. SimBiologyTM extendsMATLAB¨ with tools for modeling, simulating, and analyzing biochemicalpathways. SimBiology lets you simulate a model using stochastic ordeterministic solvers and analyze your pathway with tools such asparameter estimation and sensitivity analysis.
Sensor-Adaptive Signal Processing – Economics viewpoint
Noise
Signal
SensorSignal Processing
Estimate
Feedback (Stochastic Control)
Topic 3: Global Game for
Sensor Activation
! Gives a communication free
deployment protocol.
! Simple threshold policies are in Nash
equilibrium.
! If uncertainty is high or congestion is low.
! Compare with other approaches:
! Local communication & self-organization
[Biswas & Phoba 06, Clare & Pottie 99,
Aroraa et. al. 04]
! Flocking
!"#$%&'$()*"+(),-.
/*)&
!-#,01#&
How can agents autonomously manage their behavior?Game theory can be used as a synthesis and analysis tool. How cansimple behavior by agents result in sophisticated global behavior? How toachieve consensus in decisions? (S. Hart, Econmetrica, 2005).
How can agents learn from actions of other agents?Social learning results in herding behavior.In 1995, Treacy & Wiersema secretly bought 50,000 copies of their ownbook. Made NY times best seller list. (Rational Herds, Chamely, 2004)
How do local decisions affect global decision making?Example: Sequential change detection has multiple threshold policy
decision
posterior probability of change
declare change
no changepolicy
Sensor-Adaptive Signal Processing – Economics viewpoint
Noise
Signal
SensorSignal Processing
Estimate
Feedback (Stochastic Control)
Topic 3: Global Game for
Sensor Activation
! Gives a communication free
deployment protocol.
! Simple threshold policies are in Nash
equilibrium.
! If uncertainty is high or congestion is low.
! Compare with other approaches:
! Local communication & self-organization
[Biswas & Phoba 06, Clare & Pottie 99,
Aroraa et. al. 04]
! Flocking
!"#$%&'$()*"+(),-.
/*)&
!-#,01#&
How can agents autonomously manage their behavior?Game theory can be used as a synthesis and analysis tool. How cansimple behavior by agents result in sophisticated global behavior? How toachieve consensus in decisions? (S. Hart, Econmetrica, 2005).
How can agents learn from actions of other agents?Social learning results in herding behavior.In 1995, Treacy & Wiersema secretly bought 50,000 copies of their ownbook. Made NY times best seller list. (Rational Herds, Chamely, 2004)
How do local decisions affect global decision making?Example: Sequential change detection has multiple threshold policy