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Course Outline

Introduction to Multi-Agent Systems

  • General overview of Multi-Agent Systems (MAS)
  • Practical applications of MAS across various domains
  • Contrast with single-agent systems

Structures for Multi-Agent Systems

  • Centralized versus decentralized structures
  • Hybrid and layered methodologies for MAS
  • Resources and tools for MAS development (e.g., JADE, SPADE)

Agent Interaction and Alignment

  • Interaction protocols and languages (e.g., FIPA ACL)
  • Alignment methods: planning, negotiation, and synchronization
  • Spontaneous behavior and self-organization in MAS

Game Theory and Choice Making

  • Foundations of game theory for MAS
  • Cooperative versus competitive strategies
  • Settling disputes among agents

Learning in Multi-Agent Systems

  • Reinforcement learning within MAS
  • Collaborative and adversarial learning dynamics
  • Knowledge transfer and sharing among agents

Challenges and Advanced Topics

  • Expandability and performance in extensive MAS environments
  • Reliability and security in agent interaction
  • Ethical aspects and implications of MAS development

Practical Activities

  • Constructing a fundamental MAS for resource distribution
  • Simulating agent interaction and alignment in a dynamic setting
  • Deploying a MAS using a tool such as JADE

Summary and Future Directions

Requirements

  • Strong grasp of artificial intelligence fundamentals
  • Competence in Python programming
  • Knowledge of game theory and distributed systems (suggested)

Target Audience

  • AI researchers
  • AI engineers
 14 Hours

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