Course Outline

Introduction to Multi-Agent Systems

  • Defining multi-agent systems and their applications
  • Role of Agentic AI in autonomous agent interactions
  • Challenges in multi-agent coordination

Developing Agentic AI for Multi-Agent Environments

  • Designing autonomous AI agents
  • Agent communication and decision-making strategies
  • Simulation environments for multi-agent AI

Reinforcement Learning for Agentic AI

  • Applying reinforcement learning to multi-agent systems
  • Training autonomous agents for adaptive behavior
  • Balancing exploration and exploitation in decision-making

Collaboration and Competition in Multi-Agent Systems

  • Cooperative AI agent strategies
  • Competitive and adversarial AI interactions
  • Emergent behaviors in multi-agent environments

Agentic AI in Robotics and Automation

  • Multi-agent coordination in robotics
  • Swarm intelligence and decentralized decision-making
  • Case studies in robotic AI applications

Agentic AI in Game Development

  • Designing AI-driven NPCs in multi-agent simulations
  • Behavior modeling for interactive AI agents
  • Real-time AI decision-making in dynamic environments

Scaling Multi-Agent AI Systems

  • Performance optimization for large-scale AI interactions
  • Managing agent hierarchies and role-based decision-making
  • Integrating AI agents with cloud-based environments

Future of Multi-Agent Systems with Agentic AI

  • Emerging trends in autonomous AI collaboration
  • Expanding multi-agent AI capabilities with deep learning
  • Ethical and regulatory considerations for multi-agent AI

Summary and Next Steps

Requirements

  • Experience with AI model development
  • Understanding of multi-agent system concepts
  • Familiarity with reinforcement learning and AI-driven automation

Audience

  • AI researchers studying autonomous agent interactions
  • Robotics engineers designing multi-agent coordination
  • Game developers implementing AI-driven NPC behavior
 14 Hours

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