To provide an understanding of how a wide range of decision making problems, arising in control engineering and other areas of systems science, can be formulated as games; to convey the different concepts of solution to a game, and to cover the principal techniques for obtaining these solutions..

1. Formulate decision making problems in systems science, involving multiple players, as games.
2. Understand the importance of information patterns in the precise formulation of a game.
3. Identify the different special class of games and their distinct features, including zero-sum games, dynamic/stochastic dynamic games.
4. Understand solution concepts for games problems, including Nash equilibria, mixed strategies and Stackelberg solutions.
5. Understand how to use dynamic programming techniques to solve deterministic/stochastic dynamic games
6. Design robust feedback controls, based on solving a dynamic game.

1. Zero Sum Games: pure and mixed strategies, existence of saddle points, algorithms for solving matrix games.
2. Non-cooperative, non-zero sum games: information patterns, normal and exterior forms, Nash equilibria, Stackelberg solutions and their computation.
3. Deterministic dynamic games: optimal control, dynamic programming solution techniques, LQ discrete time games.
4. Stochastic dynamic games: optimal stochastic control, stochastic dynamic programming.
5. Application of dynamic games to robust control system design.