TDDE13 - Multiagent Systems

Responsible for laboratory sessions and seminars in this advanced course on multi-agent systems. The course covers agentic AI with hands-on labs on building LLM agents using local open-source models (Ollama with llama3.2) and multi-agent communication and coordination. Additionally, I supervise multi-agent reinforcement learning laboratory sessions where students implement and experiment with MARL algorithms. I also facilitate seminars on agents and game theory, mechanism design, auctions, and social choice, guiding students through exercise sets and providing feedback on their individual research reports. The course emphasizes both theoretical foundations of multi-agent systems and practical implementation skills, preparing students for research and industry applications in collaborative AI systems.

Lab Responsibilities

• Lab 1a: Building Your First LLM Agent - Guide students in implementing LLM agents using Ollama with llama3.2 models
• Lab 1b: Multiagent Communication & Coordination - Supervise students in developing multi-agent communication protocols
• Multi-agent Reinforcement Learning Lab - Facilitate hands-on implementation of MARL algorithms

Seminar Responsibilities

• Exercise Set 1 - Agents and Game Theory - Guide students through problem sets and facilitate discussions
• Exercise Set 2 - Mechanism Design, Auctions and Social Choice - Supervise exercises and provide feedback
• Individual Report Supervision - Review and provide feedback on student research reports on multi-agent systems topics