Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Foundations of Multi-Agent Systems
- Examining agents, their environments, and interaction paradigms
- Analyzing cooperation, competition, and autonomy within agentic frameworks
- Real-world applications in logistics, robotics, and strategic decision-making
Essentials of Agent Architecture
- Distinguishing between reactive and deliberative agent models
- Defining communication protocols and coordination methodologies
- Handling knowledge representation and shared state management
Python Implementation of Agents
- Constructing agents utilizing the Mesa framework
- Modeling complex environments and agent interactions
- Simulating agent behaviors and generating visual insights
Coordination and Communication Mechanisms
- Architecting message passing and shared memory structures
- Implementing negotiation, consensus building, and task allocation
- Applying coordination algorithms such as contract net, market-based, and swarm models
Adaptation and Learning in Multi-Agent Contexts
- Implementing reinforcement learning for multiple interacting agents
- Exploring cooperative versus competitive learning dynamics
- Utilizing PettingZoo and Stable-Baselines3 for Multi-Agent Reinforcement Learning (MARL)
Distributed Computing and Scalability
- Leveraging Ray for scalable, distributed multi-agent simulations
- Managing concurrency and synchronization challenges
- Optimizing parallel computation and shared resource handling
Human–Agent Collaboration Strategies
- Designing interfaces for human-in-the-loop coordination
- Creating hybrid workflows featuring AI-assisted decision support
- Navigating ethical and operational considerations
Capstone Project
- Designing and implementing a comprehensive multi-agent system in Python
- Demonstrating effective coordination and learning among agents
- Presentation of simulation outcomes and performance analysis
Conclusion and Future Directions
Requirements
- Advanced proficiency in Python programming
- Solid comprehension of reinforcement learning or AI agent design principles
- Working knowledge of distributed systems and networking fundamentals
Target Audience
- System architects responsible for designing collaborative or distributed AI infrastructures
- Researchers investigating coordination mechanisms and collective intelligence
- Engineers building hybrid human–agent or multi-agent operational workflows
28 Hours
