Inverse Optimization for Policy Identification 

Student(en): 
Betreuer: Chithrupa Ramesh, Marius Schmitt, Peyman Mohajerin Esfahani 
Beschreibung: In datadriven inverse optimization an observer aims to learn the preferences of an agent who solves a parametric optimization problem depending on an exogenous signal. We are interested in the visual search problem, where agents identify the location of a target symbol in an N×N visual array. By observing actions of expert agents, we wish to use inverse optimisation to learn the underlying cost function for this problem. We use an existing visual search policy in lieu of an expert agent, to generate observations for the inverseoptimization problem. Our goal is to compare the estimated decisions with the decisions taken using the policy. Weitere Informationen 
Professor: John Lygeros 
Projektcharakteristik: Typ: Art der Arbeit: Voraussetzungen: Optimisation Theory and Basic Control Theory, some knowledge of Markov processes will be useful.  
Anzahl StudentInnen: Status: taken  
Projektstart: July 1st, 2016 Semester: 