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.
Duration 14 hours
Course Outline
Foundations of Multi-Agent Systems
- Broad overview of Multi-Agent Systems (MAS)
- Real-world applications of MAS across various industries
- Contrasting MAS with single-agent systems
Architectural Models for Multi-Agent Systems
- Evaluating centralized versus decentralized architectures
- Exploring hybrid and layered MAS methodologies
- Reviewing development tools and frameworks (e.g., JADE, SPADE)
Agent Communication and Coordination Mechanisms
- Examining communication protocols and languages (e.g., FIPA ACL)
- Mastering coordination techniques including planning, negotiation, and synchronization
- Analyzing emergent behavior and self-organization within MAS
Game Theory and Strategic Decision-Making
- Core game theory concepts applied to MAS
- Distinguishing between cooperative and competitive strategies
- Methods for resolving inter-agent conflicts
Learning Dynamics in Multi-Agent Systems
- Integrating reinforcement learning into MAS
- Understanding collaborative and adversarial learning dynamics
- Leveraging transfer learning and inter-agent knowledge sharing
Advanced Topics and Systemic Challenges
- Ensuring scalability and performance in large-scale MAS
- Enhancing trust and security in agent interactions
- Considering ethical implications of MAS development
Practical Application Exercises
- Building a foundational MAS for resource allocation tasks
- Simulating agent communication and coordination in dynamic scenarios
- Deploying a MAS using a framework such as JADE
Recap and Future Directions
Requirements
- A robust grasp of core artificial intelligence concepts
- Strong proficiency in Python programming
- Working knowledge of game theory and distributed systems (suggested)
Intended Audience
- AI researchers
- AI engineers
