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Course Outline
Introduction to Multi-Agent Systems
- Overview of Multi-Agent Systems (MAS).
- Applications of MAS in real-world domains.
- Comparison with single-agent systems.
Architectures for Multi-Agent Systems
- Centralized versus decentralized architectures.
- Hybrid and layered approaches to MAS.
- Tools and frameworks for MAS development (e.g., JADE, SPADE).
Agent Communication and Coordination
- Communication protocols and languages (e.g., FIPA ACL).
- Coordination techniques: planning, negotiation, and synchronization.
- Emergent behaviour and self-organization in MAS.
Game Theory and Decision Making
- Basics of game theory for MAS.
- Cooperative versus competitive strategies.
- Resolving conflicts among agents.
Learning in Multi-Agent Systems
- Reinforcement learning in MAS.
- Collaborative and adversarial learning dynamics.
- Transfer learning and knowledge sharing among agents.
Challenges and Advanced Topics
- Scalability and performance in large MAS environments.
- Trust and security in agent communication.
- Ethical considerations and implications of MAS development.
Hands-On Activities
- Implementing a basic MAS for resource allocation.
- Simulating agent communication and coordination in a dynamic environment.
- Deploying a MAS using a framework like JADE.
Summary and Next Steps
Requirements
- A robust understanding of artificial intelligence concepts.
- Proficiency in Python programming.
- Familiarity with game theory and distributed systems (recommended).
Target Audience
- AI researchers.
- AI engineers.
14 Hours
