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Duration 14 hours
Course Outline
Introduction to Multi-Agent Systems
- General overview of Multi-Agent Systems (MAS)
- Real-world application areas of MAS
- Differentiating MAS from single-agent systems
Architectural Models for Multi-Agent Systems
- Evaluating centralized versus decentralized architectures
- Hybrid and layered strategies for MAS
- Development tools and frameworks for MAS (e.g., JADE, SPADE)
Agent Communication and Coordination Mechanisms
- Communication protocols and languages (e.g., FIPA ACL)
- Coordination methods: planning, negotiation, and synchronization
- Self-organization and emergent behavior within MAS
Game Theory and Decision Processes
- Foundations of game theory applied to MAS
- Comparing cooperative and competitive strategies
- Strategies for resolving inter-agent conflicts
Learning Mechanisms in Multi-Agent Systems
- Application of reinforcement learning in MAS
- Dynamics of collaborative and adversarial learning
- Knowledge transfer and sharing between agents
Advanced Challenges and Topics
- Performance and scalability in large-scale MAS environments
- Security and trust in agent communication
- Ethical implications and considerations in MAS development
Practical Exercises
- Building a basic MAS for resource allocation
- Simulating agent communication and coordination in dynamic settings
- Deploying a MAS using a framework such as JADE
Summary and Future Directions
Requirements
- Strong grasp of core artificial intelligence concepts
- Strong proficiency in Python programming
- Background in game theory and distributed systems (preferred)
Target Audience
- AI researchers
- AI engineers
