Description

Understand the mathematical foundations of strategic decision-making in multi-player environments, including concepts such as Pareto optimality and Nash equilibrium. Apply advanced game-theoretic techniques, including mixed-strategy Nash equilibria and Bayesian games, to analyze complex strategic interactions. Demonstrate proficiency in modeling and analyzing strategic situations using mathematical tools and frameworks. Evaluate the role of artificial intelligence in optimizing strategies and decision-making processes in dynamic environments such as auctions, marketplaces, and social networks. Apply AI algorithms and techniques to solve strategic decision-making problems, including optimization, prediction, and learning. Critically assess the limitations and challenges of applying game theory and AI in real-world settings, considering factors such as incomplete information, uncertainty, and strategic behavior. Synthesize and communicate complex concepts related to strategic decision-making and AI through written reports, presentations, and discussions. Explore interdisciplinary connections between game theory, artificial intelligence, and other fields such as economics, computer science, and social sciences.

Program

Artificial Intelligence 132 CRs

Objectives

• This course focuses on Marrying the mathematical study of strategic decision making with AI, this course delves into strategies for multiple-player situations. Topics include Pareto optimality, mixed- strategy Nash equilibriums, and Bayesian games. This course explores the role of AI in optimizing strategies in environments like auctions, marketplaces, and social networks.