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Course Outline
Introduction to Multi-Agent Systems
- Overview of agents, environments, and interaction models
- Dynamics of cooperation, competition, and autonomy within agentic systems
- Practical applications in logistics, robotics, and decision-making
Core Concepts of Agent Architecture
- Distinguishing between reactive and deliberative agents
- Communication protocols and various coordination models
- Knowledge representation and the management of shared state
Implementing Agents in Python
- Constructing agents using the Mesa framework
- Modeling environments and defining interactions
- Simulating agent behavior and generating visualizations
Coordination and Communication
- Architectures for message passing and shared memory
- Processes for negotiation, reaching consensus, and task allocation
- Implementation of coordination algorithms, including contract net, market-based, and swarm models
Learning and Adaptation in Multi-Agent Systems
- Applying reinforcement learning to multiple agents
- Analyzing cooperative versus competitive learning dynamics
- Utilizing PettingZoo and Stable-Baselines3 for multi-agent reinforcement learning (MARL)
Distributed Computing and Scaling
- Leveraging Ray for distributed multi-agent simulations
- Managing concurrency and synchronization processes
- Parallelizing computation and managing shared resources
Human–Agent Collaboration
- Designing interfaces for human-in-the-loop coordination
- Implementing hybrid workflows with AI-assisted decision support
- Ethical and operational considerations in deployment
Capstone Project
- Designing and implementing a complete multi-agent system in Python
- Demonstrating effective coordination and learning among agents
- Presenting simulation results and key performance insights
Summary and Next Steps
Requirements
- Advanced proficiency in Python programming
- Solid understanding of reinforcement learning or AI agent design
- Familiarity with distributed systems and networking principles
Target Audience
- System architects focused on designing collaborative or distributed AI systems
- Researchers investigating coordination and collective intelligence
- Engineers developing hybrid human–agent or multi-agent workflows
28 Hours
