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Course Outline
Introduction to Multi-Agent Systems
- Overview of Multi-Agent Systems (MAS)
- Real-world applications of MAS across various domains
- Contrast with single-agent systems
Architectural Design for Multi-Agent Systems
- Centralized versus decentralized architectures
- Hybrid and layered architectural approaches
- Development tools and frameworks (e.g., JADE, SPADE)
Agent Communication and Coordination
- Protocols and languages for communication (e.g., FIPA ACL)
- Coordination methods: planning, negotiation, and synchronization
- Emergent behavior and self-organization mechanisms
Game Theory and Strategic Decision Making
- Foundations of game theory applied to MAS
- Cooperative versus competitive strategies
- Techniques for resolving inter-agent conflicts
Learning Dynamics in Multi-Agent Systems
- Reinforcement learning within MAS contexts
- Collaborative and adversarial learning processes
- Transfer learning and knowledge exchange among agents
Advanced Topics and Challenges
- Scalability and performance optimization in large-scale MAS
- Trust and security in agent communications
- Ethical considerations and broader implications of MAS
Practical Labs
- Building a basic MAS for resource allocation
- Simulating communication and coordination in dynamic settings
- Deploying MAS using frameworks such as JADE
Conclusion and Future Directions
Requirements
- A strong command of artificial intelligence concepts
- Strong proficiency in Python programming
- Recommended familiarity with game theory and distributed systems
Target Audience
- AI Researchers
- AI Engineers
14 Hours
