Developing Multi-Agent Systems

• Solid understanding of artificial intelligence concepts
• Proficiency in Python programming
• Familiarity with game theory and distributed systems (recommended)

Audience

• AI researchers
• AI engineers

Multi-Agent Systems (MAS) are a cutting-edge area of artificial intelligence where multiple AI agents collaborate or compete within dynamic environments.

This instructor-led, live training (online or onsite) is aimed at advanced-level AI professionals who wish to master the skills to design, build, and deploy MAS that solve complex, real-world problems.

By the end of this training, participants will be able to:

• Understand the principles of multi-agent system architectures.
• Implement strategies for communication, coordination, and decision-making in MAS.
• Apply game theory to model agent interactions and resolve conflicts.
• Leverage frameworks like JADE to create scalable MAS solutions.
• Address challenges like scalability, trust, and emergent behavior in MAS.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to Multi-Agent Systems

• Overview of Multi-Agent Systems (MAS)
• Applications of MAS in real-world domains
• Comparison with single-agent systems

Architectures for Multi-Agent Systems

• Centralized vs decentralized architectures
• Hybrid and layered approaches to MAS
• Tools and frameworks for MAS development (e.g., JADE, SPADE)

Agent Communication and Coordination

• Communication protocols and languages (e.g., FIPA ACL)
• Coordination techniques: planning, negotiation, and synchronization
• Emergent behavior and self-organization in MAS

Game Theory and Decision Making

• Basics of game theory for MAS
• Cooperative vs competitive strategies
• Resolving conflicts among agents

Learning in Multi-Agent Systems

• Reinforcement learning in MAS
• Collaborative and adversarial learning dynamics
• Transfer learning and knowledge sharing among agents

Challenges and Advanced Topics

• Scalability and performance in large MAS environments
• Trust and security in agent communication
• Ethical considerations and implications of MAS development

Hands-On Activities

• Implementing a basic MAS for resource allocation
• Simulating agent communication and coordination in a dynamic environment
• Deploying a MAS using a framework like JADE

Summary and Next Steps