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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
