Master’s Thesis Decision Making Under Uncertainty: A Robust Optimization

Conventional approaches for decision making often assume that access to full information is possible. Nevertheless, such ex-plicit knowledge about the model’s dynamics is seldom available in practical applications. In this thesis the problem of the con-struction of a plan for a sequence of decisions under an uncer-tain adversarial environment is addressed. The uncertainty of the information is modeled via a set of sequential Markov deci-sion processes and a number of methods are utilized in order to produce a robust plan, depending on the setting. Additionally, the intractability of the computation of an exact solution, with the Cutting Plane method, is shown, in the case where policy value hyperplanes are viewed as potential cuts