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Course Outline
Introduction to Multi-Agent Systems
- Defining multi-agent systems and their practical applications.
- The role of Agentic AI in facilitating autonomous agent interactions.
- Key challenges associated with multi-agent coordination.
Developing Agentic AI for Multi-Agent Environments
- Designing autonomous AI agents.
- Strategies for agent communication and decision-making.
- Utilizing simulation environments for multi-agent AI testing.
Reinforcement Learning for Agentic AI
- Applying reinforcement learning techniques to multi-agent systems.
- Training autonomous agents to exhibit adaptive behaviours.
- Balancing exploration and exploitation during decision-making processes.
Collaboration and Competition in Multi-Agent Systems
- Strategies for cooperative AI agents.
- Navigating competitive and adversarial AI interactions.
- Observing emergent behaviours in multi-agent environments.
Agentic AI in Robotics and Automation
- Coordinating multiple agents in robotic systems.
- Leveraging swarm intelligence and decentralized decision-making.
- Reviewing case studies on robotic AI applications.
Agentic AI in Game Development
- Designing AI-driven NPCs within multi-agent simulations.
- Modelling behaviours for interactive AI agents.
- Enabling real-time AI decision-making in dynamic settings.
Scaling Multi-Agent AI Systems
- Optimizing performance for large-scale AI interactions.
- Managing agent hierarchies and role-based decision-making structures.
- 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 through deep learning.
- Ethical and regulatory considerations for multi-agent AI.
Summary and Next Steps
Requirements
- Prior experience in developing AI models.
- A solid understanding of multi-agent system concepts.
- Familiarity with reinforcement learning and AI-driven automation techniques.
Intended Audience
- AI researchers investigating interactions among autonomous agents.
- Robotics engineers focused on designing multi-agent coordination mechanisms.
- Game developers implementing AI-controlled NPC behaviours.
14 Hours
Testimonials (1)
practical exercises
