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Course Outline
Foundations of Multi-Agent Systems
- Survey of agents, their environments, and interaction paradigms
- Exploring cooperation, competition, and autonomy in agentic systems
- Real-world applications in logistics, robotics, and strategic decision-making
Fundamentals of Agent Architecture
- Distinguishing between reactive and deliberative agents
- Defining communication protocols and coordination mechanisms
- Managing knowledge representation and shared system state
Python Implementation for Agents
- Constructing agents utilizing the Mesa framework
- Modeling dynamic environments and agent interactions
- Simulating agent behaviors and generating visual outputs
Orchestration and Communication Strategies
- Architectures for message passing and shared memory
- Techniques for negotiation, achieving consensus, and allocating tasks
- Coordination algorithms including contract net, market-based, and swarm models
Learning and Adaptation in Multi-Agent Contexts
- Applying reinforcement learning to multiple agents
- Analyzing cooperative versus competitive learning dynamics
- Employing PettingZoo and Stable-Baselines3 for Multi-Agent Reinforcement Learning
Distributed Computing and Scalability
- Leveraging Ray for distributed multi-agent simulations
- Handling concurrency and synchronization issues
- Optimizing parallel computation and managing shared resources
Collaboration Between Humans and Agents
- Designing interfaces for human-in-the-loop coordination
- Building hybrid workflows with AI-assisted decision support
- Addressing ethical and operational implications
Capstone Project
- Design and build a complete multi-agent system in Python
- Demonstrate effective coordination and learning among agents
- Present simulation outcomes and analyze performance metrics
Wrap-up and Future Directions
Requirements
- Advanced proficiency in Python programming
- Solid understanding of reinforcement learning or AI agent design patterns
- Working knowledge of distributed systems and networking fundamentals
Target Audience
- System architects building collaborative or distributed AI infrastructures
- Researchers focused on coordination mechanisms and collective intelligence
- Engineers creating hybrid human–agent or multi-agent operational workflows
28 Hours
