Table of Contents
Multi-Agent Systems is a fascinating and crucial field in modern artificial intelligence, with extensive applications and intersections with multiple disciplines. This course offers a systematic introduction to the field, equipping learners with in-depth knowledge and practical skills.
Course Objectives:
- Master the basic concepts, properties, theories, and methods of game theory.
- Master the core techniques and methods for multi-agent decision-making.
- Master modern LLM-based multi-agent system methods with practical usage skills.
- Implement algorithms and apply them to practical problems.
Course Structure: 1/3 Game Theory Foundations + 1/3 Multi-Agent Learning + 1/3 LLM-based Multi-Agent Systems
Prerequisite Knowledge:
- Calculus, Linear Algebra, Probability Theory.
- Basics of Artificial Intelligence / Machine Learning (Supervised Learning).
- Python programming and the usage of related tools / libraries.
- Basic experience in GPU training.
Contact Info
- Instructor
- Yaodong Yang (yaodong.yang@pku.edu.cn)