This course introduces foundational concepts and solution strategies for designing intelligent systems composed of multiple autonomous agents. Students will learn about agent-based modeling, coordination, communication, and decision-making in cooperative and competitive environments. The course introduces core theories and algorithms for multi-agent decision-making. It also covers how contemporary AI techniques—such as deep learning and generative models—can be applied to enhance multi-agent system capabilities. Through hands-on projects, students will gain practical experience in analyzing and designing multi-agent systems for real-world applications.
Basic knowledge of linear algebra, probability theory, and Python programming. Familiarity with machine learning and deep learning equivalent to (DS 3010, CS 4445, CS 4342). Familiarity with deep learning equivalent to CS/DS 4343 encouraged.