A graphical modeling approach to simplifying sequential teams

Authors:
Aditya Mahajan;Sekhar Tatikonda
Affiliations:
Department of Electrical Engineering, Yale University, New Haven, CT;Department of Electrical Engineering, Yale University, New Haven, CT
Venue:
WiOPT'09 Proceedings of the 7th international conference on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks
Year:
2009

Citing 9
Cited 0

The decentralized Wald problem

Information and Computation
Topological sorting of large networks

Communications of the ACM
Optimal Sequential Vector Quantization of Markov Sources

SIAM Journal on Control and Optimization
d-Separation: From Theorems to Algorithms

UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Sequential decomposition of sequential dynamic teams: applications to real-time communication and networked control systems

Sequential decomposition of sequential dynamic teams: applications to real-time communication and networked control systems
Optimal Performance of Networked Control Systems with Nonclassical Information Structures

SIAM Journal on Control and Optimization
Bayes-ball: Rational pastime (for determining irrelevance and requisite information in belief networks and influence diagrams)

UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
On the Structure of Optimal Real-Time Encoders and Decoders in Noisy Communication

IEEE Transactions on Information Theory
On the design of globally optimal communication strategies for real-time noisy communication systems with noisy feedback

IEEE Journal on Selected Areas in Communications

Quantified Score

Hi-index	0.00

Visualization

Abstract

A graphical model for sequential teams is presented. This model is easy to understand, and at the same time, is general enough to model any finite horizon sequential team with finite valued system variables and unconstrained decision rules. The model can also be represented as a directed acyclic factor graph. This representation makes it easier to visualize and understand the functional dependencies between different system variables. It also helps in identifying data that is irrelevant for a decision maker to take an optimal decision. Such irrelevant data can be identified using algorithms from graphical models. Thus, the structural properties of optimal decision makers in this model for a sequential team can be identified in an automated manner using the directed acyclic factor graph representation of the sequential team.