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Course Outline
Foundations of Multi-Agent Systems
- Overview of Multi-Agent Systems (MAS)
- Real-world applications of MAS across various domains
- Contrasting MAS with single-agent systems
Architectural Patterns for MAS
- Centralized versus decentralized structures
- Hybrid and layered methodologies for MAS
- Development tools and frameworks (e.g., JADE, SPADE)
Agent Interaction and Coordination
- Communication protocols and languages (e.g., FIPA ACL)
- Coordination methods: planning, negotiation, and synchronization
- Emergent phenomena and self-organization within MAS
Game Theory and Strategic Decision Making
- Foundational game theory principles applied to MAS
- Cooperative versus competitive strategies
- Conflict resolution mechanisms among agents
Learning Dynamics in MAS
- Reinforcement learning applications in MAS
- Collaborative and adversarial learning environments
- Transfer learning and knowledge exchange between agents
Complexities and Advanced Perspectives
- Scalability and performance optimization in large-scale MAS
- Trust and security in agent communications
- Ethical considerations and the broader impact of MAS development
Practical Workshops
- Developing a basic MAS for resource allocation
- Simulating agent communication and coordination in dynamic settings
- Deploying a MAS utilizing a framework such as JADE
Recap and Future Directions
Requirements
- A robust command of core artificial intelligence concepts
- Advanced proficiency in Python programming
- Knowledge of game theory and distributed systems (advisable)
Target Audience
- AI researchers
- AI engineers
14 Hours
