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Course Outline
Foundations of Multi-Agent Systems
- Introduction to agents, their environments, and interaction models
- Dynamics of cooperation, competition, and autonomy in agentic systems
- Real-world applications in logistics, robotics, and complex decision-making
Essential Principles of Agent Architecture
- Distinguishing between reactive and deliberative agent types
- Exploring communication protocols and various coordination models
- Techniques for knowledge representation and managing shared state
Building Agents in Python
- Constructing agents utilizing the Mesa framework
- Modeling complex environments and agent interactions
- Simulating agent behaviors and generating visualizations
Strategies for Coordination and Communication
- Architectures involving message passing and shared memory
- Mechanisms for negotiation, consensus building, and task allocation
- Advanced coordination algorithms, including contract net, market-based, and swarm models
Learning and Adaptation within Multi-Agent Frameworks
- Applying reinforcement learning techniques to multiple agents
- Analyzing cooperative versus competitive learning dynamics
- Leveraging PettingZoo and Stable-Baselines3 for Multi-Agent Reinforcement Learning (MARL)
Distributed Computing and Scalability
- Utilizing Ray for scalable distributed multi-agent simulations
- Techniques for managing concurrency and synchronization
- Optimizing parallel computation and handling shared resources
Human–Agent Collaboration
- Designing interfaces for human-in-the-loop coordination
- Implementing hybrid workflows with AI-assisted decision support
- Addressing ethical and operational considerations in agent deployment
Capstone Project
- Design and deploy a complete multi-agent system in Python
- Demonstrate effective coordination and learning among agents
- Present simulation outcomes and provide performance insights
Conclusion and Future Directions
Requirements
- Advanced proficiency in Python programming
- Solid comprehension of reinforcement learning or AI agent design patterns
- Working knowledge of distributed systems and networking concepts
Target Audience
- System architects specializing in collaborative or distributed AI infrastructures
- Researchers focused on coordination mechanisms and collective intelligence
- Engineers building hybrid human–agent or multi-agent workflow solutions
28 Hours
