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Course Outline
Introduction to Multi-Agent Systems
- Overview of agents, environments, and interaction paradigms
- Dynamics of cooperation, competition, and autonomy in agentic systems
- Real-world applications in logistics, robotics, and strategic decision-making
Core Principles of Agent Architecture
- Distinguishing between reactive and deliberative agents
- Communication protocols and coordination frameworks
- Knowledge representation techniques and shared state management
Building Agents with Python
- Constructing agents using the Mesa framework
- Modeling complex environments and agent interactions
- Simulating agent behavior and visualizing outcomes
Coordination and Communication Strategies
- Message passing mechanisms and shared memory architectures
- Negotiation protocols, consensus building, and task allocation
- Coordination algorithms including contract net, market-based, and swarm models
Learning and Adaptation in Multi-Agent Systems
- Applying reinforcement learning to multi-agent scenarios
- Analyzing cooperative versus competitive learning dynamics
- Utilizing PettingZoo and Stable-Baselines3 for Multi-Agent Reinforcement Learning (MARL)
Distributed Computing and Scalability
- Leveraging Ray for distributed multi-agent simulations
- Managing concurrency and synchronization effectively
- Parallelizing computational tasks and handling shared resources
Human–Agent Collaboration
- Designing interfaces for human-in-the-loop coordination
- Developing hybrid workflows with AI-assisted decision support
- Ethical and operational considerations in collaborative systems
Capstone Project
- Design and implement a comprehensive multi-agent system in Python
- Demonstrate effective coordination and learning among agents
- Present simulation results and derive performance insights
Summary and Next Steps
Requirements
- Advanced proficiency in Python programming
- Solid understanding of reinforcement learning or AI agent design principles
- Awareness of distributed systems and networking fundamentals
Target Audience
- System architects designing collaborative or distributed AI architectures
- Researchers specializing in coordination and collective intelligence
- Engineers developing hybrid human–agent workflows or multi-agent systems
28 Hours
