PhD Defense by Yanling Chang

Event Details
  • Date/Time:
    • Thursday October 22, 2015
      9:30 am - 11:30 am
  • Location: ISYE MAIN building Poole Board Room 236
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Summary Sentence: A LEADER-FOLLOWER PARTIALLY OBSERVED MARKOV GAME

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Title: A LEADER-FOLLOWER PARTIALLY OBSERVED MARKOV GAME

 

Advisors: Dr. Chelsea C. White III and Dr. Alan L. Erera

 

Committee members:

 Dr. Turgay Ayer

 Dr. Enlu Zhou

 Dr. Luca Dieci (School of Mathematics)

 

Location: ISYE MAIN building Poole Board Room 236

 

Date and Time: October 22nd, 9:30 AM

 

Abstract: 

 

Models of sequential decision-making under uncertainty provide a rich normative framework for one or more intelligent decision-makers to improve, e.g., optimize, the operation of a system subject to control over a horizon containing a sequence of decision epochs. The solutions of such models can provide guidance as to how decision-makers should select actions, based on currently available data, in order to achieve their objectives. This dissertation models and analyzes a sequential stochastic game involving two intelligent and adaptive decision-makers.  Each of these decision-makers partially observes the other decision-maker's state at each decision epoch. 

 

Chapter II presents a model of and analyzes a leader-follower, multi-objective partially observed infinite horizon Markov game, where it is assumed that the follower selects its policy with complete knowledge of the policy selected by the leader. We show how the results of this POMG can be used to support decision-making involving a leader having multiple objectives.

 

Chapter III considers the single objective version of the problem considered in Chapter II and investigates the impact of how accurately the leader observes the follower's state on the performance of the leader, thus representing an analysis of the value of information for this class of POMGs.

 

Chapter IV applies the results of the first two chapters in order to quantify the risk of a food production facility to an intelligent and adaptive adversary intent on delivering a chemical or biological toxin to the general population through use of the food supply chain. The goal of this chapter is to develop a new model of dynamic risk analysis that can explicitly describe the strategic interaction between two intelligent and adaptive agents with different objectives, and to provide decision support to the defender as to when and what action should be taken in order to achieve the defender's (possibly multiple) objectives.

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Phd Defense
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  • Created By: Tatianna Richardson
  • Workflow Status: Published
  • Created On: Oct 6, 2015 - 9:07am
  • Last Updated: Oct 7, 2016 - 10:14pm