Course Outline

Introduction to Multi-Agent Systems

  • Overview of agents, environments, and interaction models
  • Cooperation, competition, and autonomy in agentic systems
  • Applications in logistics, robotics, and decision-making

Core Concepts of Agent Architecture

  • Reactive vs. deliberative agents
  • Communication protocols and coordination models
  • Knowledge representation and shared state

Implementing Agents in Python

  • Building agents using the Mesa framework
  • Modeling environments and interactions
  • Simulating agent behavior and visualization

Coordination and Communication

  • Message passing and shared memory architectures
  • Negotiation, consensus, and task allocation
  • Coordination algorithms (contract net, market-based, swarm models)

Learning and Adaptation in Multi-Agent Systems

  • Reinforcement learning for multiple agents
  • Cooperative vs. competitive learning dynamics
  • Using PettingZoo and Stable-Baselines3 for MARL

Distributed Computing and Scaling

  • Using Ray for distributed multi-agent simulations
  • Managing concurrency and synchronization
  • Parallelizing computation and handling shared resources

Human–Agent Collaboration

  • Designing interfaces for human-in-the-loop coordination
  • Hybrid workflows with AI-assisted decision support
  • Ethical and operational considerations

Capstone Project

  • Design and implement a multi-agent system in Python
  • Demonstrate coordination and learning among agents
  • Present simulation results and performance insights

Summary and Next Steps

Requirements

  • Strong proficiency in Python programming
  • Good understanding of reinforcement learning or AI agent design
  • Familiarity with distributed systems and networking concepts

Audience

  • System architects designing collaborative or distributed AI systems
  • Researchers working on coordination and collective intelligence
  • Engineers developing hybrid human–agent or multi-agent workflows
 28 Hours

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