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 vs 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 behavior and self-organization in MAS

Game Theory and Decision Making

  • Basics of game theory for MAS
  • Cooperative vs 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

  • Solid understanding of artificial intelligence concepts
  • Proficiency in Python programming
  • Familiarity with game theory and distributed systems (recommended)

Audience

  • AI researchers
  • AI engineers
 14 Hours

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