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Course Outline

Introduction to Multi-Agent Systems

  • Overview of Multi-Agent Systems (MAS)
  • Real-world applications of MAS
  • Differences compared to single-agent systems

Multi-Agent System Architectures

  • Centralized versus decentralized architectures
  • Hybrid and layered approaches for MAS
  • Development tools and frameworks (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

  • Foundations of game theory for MAS
  • Cooperative versus competitive strategies
  • Conflict resolution among agents

Learning in Multi-Agent Systems

  • Reinforcement learning within MAS
  • Dynamics of collaborative and adversarial learning
  • Knowledge sharing and transfer learning among agents

Challenges and Advanced Topics

  • Scalability and performance in large-scale MAS environments
  • Trust and security in agent communications
  • Ethical considerations in MAS development

Practical Activities

  • Building a basic MAS for resource allocation
  • Simulating agent communication and coordination in dynamic settings
  • Deploying a MAS using frameworks like JADE

Summary and Next Steps

Requirements

  • A strong grasp of artificial intelligence concepts
  • Proficiency in Python programming
  • Familiarity with game theory and distributed systems (recommended)

Target Audience

  • AI researchers
  • AI engineers
 14 Hours

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Provisional Upcoming Courses (Require 5+ participants)

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