Topics determined by student and faculty interests. Topics may include model and simulation theory, validation, experiment design, output analysis, variance-reduction techniques, simulation optimization, perturbation analysis, parallel and distributed simulation, intelligent simulation systems, animation and output visualization. Course links material from optimization, intelligence systems, and complexity analysis. Formal development of the methods and complete discussion of applications, theoretical properties, and evaluation. Applications of these methods to engineering design, scheduling, signal interpretation, and machine intelligence.
Emphasis is placed on real-world planning problems.
Sequential stochastic control theory. Finite horizon, infinite horizon discounted total cost, infinite horizon undiscounted total cost, and average expected cost Markov decision processes. Problems with imperfect state information. Computational aspects and suboptimal control. Vector criterion Markov decision processes.