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課程簡介
Introduction to Multi-Agent Systems
- Overview of agents, environments, and interaction models
- Cooperation, competition, and autonomy in agentic systems
- Applications in logistics, robotics, and decision-making
Core Concepts of Agent Architecture
- Reactive vs. deliberative agents
- Communication protocols and coordination models
- Knowledge representation and shared state
Implementing Agents in Python
- Building agents using the Mesa framework
- Modeling environments and interactions
- Simulating agent behavior and visualization
Coordination and Communication
- Message passing and shared memory architectures
- Negotiation, consensus, and task allocation
- Coordination algorithms (contract net, market-based, swarm models)
Learning and Adaptation in Multi-Agent Systems
- Reinforcement learning for multiple agents
- Cooperative vs. competitive learning dynamics
- Using PettingZoo and Stable-Baselines3 for MARL
Distributed Computing and Scaling
- Using Ray for distributed multi-agent simulations
- Managing concurrency and synchronization
- Parallelizing computation and handling shared resources
Human–Agent Collaboration
- Designing interfaces for human-in-the-loop coordination
- Hybrid workflows with AI-assisted decision support
- Ethical and operational considerations
Capstone Project
- Design and implement a multi-agent system in Python
- Demonstrate coordination and learning among agents
- Present simulation results and performance insights
Summary and Next Steps
最低要求
- Strong proficiency in Python programming
- Good understanding of reinforcement learning or AI agent design
- Familiarity with distributed systems and networking concepts
Audience
- System architects designing collaborative or distributed AI systems
- Researchers working on coordination and collective intelligence
- Engineers developing hybrid human–agent or multi-agent workflows
28 小時
