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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 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

  • A 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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